Smart point cloud reconstruction of objects in visual scenes in computing environments

ABSTRACT

A mechanism is described for facilitating smart point cloud reconstruction of objects in visual scenes in computing environments. An apparatus of embodiments, as described herein, includes one or more processors including one or more graphics processors, and photo-consistency logic to perform line searches on cloud points of an object to enhance photo-consistency between multiple camera views associated with a visual hull encompassing the object in a scene captured by multiple cameras coupled to the one or more processors. The apparatus may further include refinement and application logic to perform a final reconstruction of the object based on the enhanced photo-consistency, where the scene having the reconstructed object is displayed at a display device.

FIELD

Embodiments described herein generally relate to computers. Moreparticularly, embodiments are described for facilitating smart pointcloud reconstruction of objects in visual scenes in computingenvironments.

BACKGROUND

Conventional scene reconstruction techniques are not capable ofperforming at efficient or expected levels when used in certainenvironments, such as capturing human bodies, dealing with visualeffects, moving array of cameras in sporting arenas, multiplesynchronized digital cameras, etc. Such conventional techniques oftenfail to properly scale or provide the necessary scene coverage.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments are illustrated by way of example, and not by way oflimitation, in the figures of the accompanying drawings in which likereference numerals refer to similar elements.

FIG. 1 is a block diagram of a processing system, according to anembodiment.

FIG. 2 is a block diagram of an embodiment of a processor having one ormore processor cores, an integrated memory controller, and an integratedgraphics processor.

FIG. 3 is a block diagram of a graphics processor, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores.

FIG. 4 is a block diagram of a graphics processing engine of a graphicsprocessor in accordance with some embodiments.

FIG. 5 is a block diagram of hardware logic of a graphics processor coreaccording to some embodiments.

FIG. 6A-6B illustrate thread execution logic including an array ofprocessing elements employed in a graphics processor core according tosome embodiments.

FIG. 7 is a block diagram illustrating a graphics processor instructionformats according to some embodiments.

FIG. 8 is a block diagram of another embodiment of a graphics processor.

FIG. 9A is a block diagram illustrating a graphics processor commandformat according to an embodiment.

FIG. 9B is a block diagram illustrating a graphics processor commandsequence according to an embodiment.

FIG. 10 illustrates exemplary graphics software architecture for a dataprocessing system according to some embodiments.

FIG. 11A is a block diagram illustrating an IP core development systemthat may be used to manufacture an integrated circuit to performoperations according to an embodiment.

FIG. 11B illustrates a cross-section side view of an integrated circuitpackage assembly according to some embodiments.

FIG. 12 is a block diagram illustrating an exemplary system on a chipintegrated circuit that may be fabricated using one or more IP cores,according to an embodiment.

FIGS. 13A-13B are block diagrams illustrating exemplary graphicsprocessors for use within an System on Chip (SoC), according toembodiments described herein.

FIGS. 14A-14B illustrate additional exemplary graphics processor logicaccording to embodiments described herein.

FIG. 15 illustrates a computing device hosting a point cloud scenereconstruction mechanism according to one embodiment.

FIG. 16 illustrates a point cloud scene reconstruction mechanismaccording to one embodiment.

FIG. 17A-17K illustrate a transaction sequence for facilitating pointcloud reconstruction of scenes according to one embodiment.

FIG. 18 illustrate a method for facilitating point cloud reconstructionof scenes according to one embodiment.

DETAILED DESCRIPTION

In the following description, numerous specific details are set forth.However, embodiments, as described herein, may be practiced withoutthese specific details. In other instances, well-known circuits,structures and techniques have not been shown in detail in order not toobscure the understanding of this description.

Embodiments provide for a novel technique for reconstruction of objectsin visual scenes produced through multiple synchronized digital cameras.For example, in some embodiments, point clouds are produced, as opposedto voxelized level set surfaces, since point clouds store informationfor just the surface of each reconstructed object (which is differentfrom and better than the voxelized level sets requiring volumetricstorage). Moreover, point clouds are an explicit (lagrangian)representation that can embed information about color variation thatoccurs as a result of viewing direction. In contrast, voxelized levelsets are an implicit (eulerian) representation that cannot easily embedcolor/lighting variation.

This novel technique for using point cloud representation throughout thepipeline allows for greater parallelization of the reconstructionprocess on the graphics card with a smaller memory footprint thanconventional techniques like multi-view stereo matching, depth mapestimation, graph cutes, and level set evolution, etc.

It is contemplated that terms like “request”, “query”, “job”, “work”,“work item”, and “workload” may be referenced interchangeably throughoutthis document. Similarly, an “application” or “agent” may refer to orinclude a computer program, a software application, a game, aworkstation application, etc., offered through an applicationprogramming interface (API), such as a free rendering API, such as OpenGraphics Library (OpenGL®), Open Computing Language (OpenCL®), CUDA®,DirectX® 11, DirectX® 12, etc., where “dispatch” may be interchangeablyreferred to as “work unit” or “draw” and similarly, “application” may beinterchangeably referred to as “workflow” or simply “agent”. Forexample, a workload, such as that of a three-dimensional (3D) game, mayinclude and issue any number and type of “frames” where each frame mayrepresent an image (e.g., sailboat, human face). Further, each frame mayinclude and offer any number and type of work units, where each workunit may represent a part (e.g., mast of sailboat, forehead of humanface) of the image (e.g., sailboat, human face) represented by itscorresponding frame. However, for the sake of consistency, each item maybe referenced by a single term (e.g., “dispatch”, “agent”, etc.)throughout this document.

In some embodiments, terms like “display screen” and “display surface”may be used interchangeably referring to the visible portion of adisplay device while the rest of the display device may be embedded intoa computing device, such as a smartphone, a wearable device, etc. It iscontemplated and to be noted that embodiments are not limited to anyparticular computing device, software application, hardware component,display device, display screen or surface, protocol, standard, etc. Forexample, embodiments may be applied to and used with any number and typeof real-time applications on any number and type of computers, such asdesktops, laptops, tablet computers, smartphones, head-mounted displaysand other wearable devices, and/or the like. Further, for example,rendering scenarios for efficient performance using this novel techniquemay range from simple scenarios, such as desktop compositing, to complexscenarios, such as 3D games, augmented reality applications, etc.

System Overview

FIG. 1 is a block diagram of a processing system 100, according to anembodiment. In various embodiments, the system 100 includes one or moreprocessors 102 and one or more graphics processors 108, and may be asingle processor desktop system, a multiprocessor workstation system, ora server system having a large number of processors 102 or processorcores 107. In one embodiment, the system 100 is a processing platformincorporated within a system-on-a-chip (SoC) integrated circuit for usein mobile, handheld, or embedded devices.

In one embodiment, the system 100 can include, or be incorporated withina server-based gaming platform, a game console, including a game andmedia console, a mobile gaming console, a handheld game console, or anonline game console. In some embodiments, the system 100 is a mobilephone, smart phone, tablet computing device or mobile Internet device.The processing system 100 can also include, couple with, or beintegrated within a wearable device, such as a smart watch wearabledevice, smart eyewear device, augmented reality device, or virtualreality device. In some embodiments, the processing system 100 is atelevision or set top box device having one or more processors 102 and agraphical interface generated by one or more graphics processors 108.

In some embodiments, the one or more processors 102 each include one ormore processor cores 107 to process instructions which, when executed,perform operations for system and user software. In some embodiments,each of the one or more processor cores 107 is configured to process aspecific instruction set 109. In some embodiments, instruction set 109may facilitate Complex Instruction Set Computing (CISC), ReducedInstruction Set Computing (RISC), or computing via a Very LongInstruction Word (VLIW). Multiple processor cores 107 may each process adifferent instruction set 109, which may include instructions tofacilitate the emulation of other instruction sets. Processor core 107may also include other processing devices, such a Digital SignalProcessor (DSP).

In some embodiments, the processor 102 includes cache memory 104.Depending on the architecture, the processor 102 can have a singleinternal cache or multiple levels of internal cache. In someembodiments, the cache memory is shared among various components of theprocessor 102. In some embodiments, the processor 102 also uses anexternal cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC))(not shown), which may be shared among processor cores 107 using knowncache coherency techniques. A register file 106 is additionally includedin processor 102 which may include different types of registers forstoring different types of data (e.g., integer registers, floating pointregisters, status registers, and an instruction pointer register). Someregisters may be general-purpose registers, while other registers may bespecific to the design of the processor 102.

In some embodiments, one or more processor(s) 102 are coupled with oneor more interface bus(es) 110 to transmit communication signals such asaddress, data, or control signals between processor 102 and othercomponents in the system 100. The interface bus 110, in one embodiment,can be a processor bus, such as a version of the Direct Media Interface(DMI) bus. However, processor busses are not limited to the DMI bus, andmay include one or more Peripheral Component Interconnect buses (e.g.,PCI, PCI Express), memory busses, or other types of interface busses. Inone embodiment, the processor(s) 102 include an integrated memorycontroller 116 and a platform controller hub 130. The memory controller116 facilitates communication between a memory device and othercomponents of the system 100, while the platform controller hub (PCH)130 provides connections to I/O devices via a local I/O bus.

The memory device 120 can be a dynamic random-access memory (DRAM)device, a static random-access memory (SRAM) device, flash memorydevice, phase-change memory device, or some other memory device havingsuitable performance to serve as process memory. In one embodiment, thememory device 120 can operate as system memory for the system 100, tostore data 122 and instructions 121 for use when the one or moreprocessors 102 executes an application or process. Memory controller 116also couples with an optional external graphics processor 112, which maycommunicate with the one or more graphics processors 108 in processors102 to perform graphics and media operations. In some embodiments, adisplay device 111 can connect to the processor(s) 102. The displaydevice 111 can be one or more of an internal display device, as in amobile electronic device or a laptop device or an external displaydevice attached via a display interface (e.g., DisplayPort, etc.). Inone embodiment, the display device 111 can be a head mounted display(HMD) such as a stereoscopic display device for use in virtual reality(VR) applications or augmented reality (AR) applications.

In some embodiments, the platform controller hub 130 enables peripheralsto connect to memory device 120 and processor 102 via a high-speed I/Obus. The I/O peripherals include, but are not limited to, an audiocontroller 146, a network controller 134, a firmware interface 128, awireless transceiver 126, touch sensors 125, a data storage device 124(e.g., hard disk drive, flash memory, etc.). The data storage device 124can connect via a storage interface (e.g., SATA) or via a peripheralbus, such as a Peripheral Component Interconnect bus (e.g., PCI, PCIExpress). The touch sensors 125 can include touch screen sensors,pressure sensors, or fingerprint sensors. The wireless transceiver 126can be a Wi-Fi transceiver, a Bluetooth transceiver, or a mobile networktransceiver such as a 3G, 4G, or Long Term Evolution (LTE) transceiver.The firmware interface 128 enables communication with system firmware,and can be, for example, a unified extensible firmware interface (UEFI).The network controller 134 can enable a network connection to a wirednetwork. In some embodiments, a high-performance network controller (notshown) couples with the interface bus 110. The audio controller 146, inone embodiment, is a multi-channel high definition audio controller. Inone embodiment, the system 100 includes an optional legacy I/Ocontroller 140 for coupling legacy (e.g., Personal System 2 (PS/2))devices to the system. The platform controller hub 130 can also connectto one or more Universal Serial Bus (USB) controllers 142 connect inputdevices, such as keyboard and mouse 143 combinations, a camera 144, orother USB input devices.

It will be appreciated that the system 100 shown is exemplary and notlimiting, as other types of data processing systems that are differentlyconfigured may also be used. For example, an instance of the memorycontroller 116 and platform controller hub 130 may be integrated into adiscreet external graphics processor, such as the external graphicsprocessor 112. In one embodiment, the platform controller hub 130 and/ormemory controller 116 may be external to the one or more processor(s)102. For example, the system 100 can include an external memorycontroller 116 and platform controller hub 130, which may be configuredas a memory controller hub and peripheral controller hub within a systemchipset that is in communication with the processor(s) 102.

FIG. 2 is a block diagram of an embodiment of a processor 200 having oneor more processor cores 202A-202N, an integrated memory controller 214,and an integrated graphics processor 208. Those elements of FIG. 2having the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Processor200 can include additional cores up to and including additional core202N represented by the dashed lined boxes. Each of processor cores202A-202N includes one or more internal cache units 204A-204N. In someembodiments, each processor core also has access to one or more sharedcached units 206.

The internal cache units 204A-204N and shared cache units 206 representa cache memory hierarchy within the processor 200. The cache memoryhierarchy may include at least one level of instruction and data cachewithin each processor core and one or more levels of shared mid-levelcache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), or otherlevels of cache, where the highest level of cache before external memoryis classified as the LLC. In some embodiments, cache coherency logicmaintains coherency between the various cache units 206 and 204A-204N.

In some embodiments, processor 200 may also include a set of one or morebus controller units 216 and a system agent core 210. The one or morebus controller units 216 manage a set of peripheral buses, such as oneor more PCI or PCI express busses. System agent core 210 providesmanagement functionality for the various processor components. In someembodiments, system agent core 210 includes one or more integratedmemory controllers 214 to manage access to various external memorydevices (not shown).

In some embodiments, one or more of the processor cores 202A-202Ninclude support for simultaneous multi-threading. In such embodiment,the system agent core 210 includes components for coordinating andoperating cores 202A-202N during multi-threaded processing. System agentcore 210 may additionally include a power control unit (PCU), whichincludes logic and components to regulate the power state of processorcores 202A-202N and graphics processor 208.

In some embodiments, processor 200 additionally includes graphicsprocessor 208 to execute graphics processing operations. In someembodiments, the graphics processor 208 couples with the set of sharedcache units 206, and the system agent core 210, including the one ormore integrated memory controllers 214. In some embodiments, the systemagent core 210 also includes a display controller 211 to drive graphicsprocessor output to one or more coupled displays. In some embodiments,display controller 211 may also be a separate module coupled with thegraphics processor via at least one interconnect, or may be integratedwithin the graphics processor 208.

In some embodiments, a ring based interconnect unit 212 is used tocouple the internal components of the processor 200. However, analternative interconnect unit may be used, such as a point-to-pointinterconnect, a switched interconnect, or other techniques, includingtechniques well known in the art. In some embodiments, graphicsprocessor 208 couples with the ring interconnect 212 via an I/O link213.

The exemplary I/O link 213 represents at least one of multiple varietiesof I/O interconnects, including an on package I/O interconnect whichfacilitates communication between various processor components and ahigh-performance embedded memory module 218, such as an eDRAM module. Insome embodiments, each of the processor cores 202A-202N and graphicsprocessor 208 use embedded memory modules 218 as a shared Last LevelCache.

In some embodiments, processor cores 202A-202N are homogenous coresexecuting the same instruction set architecture. In another embodiment,processor cores 202A-202N are heterogeneous in terms of instruction setarchitecture (ISA), where one or more of processor cores 202A-202Nexecute a first instruction set, while at least one of the other coresexecutes a subset of the first instruction set or a differentinstruction set. In one embodiment processor cores 202A-202N areheterogeneous in terms of microarchitecture, where one or more coreshaving a relatively higher power consumption couple with one or morepower cores having a lower power consumption. Additionally, processor200 can be implemented on one or more chips or as an SoC integratedcircuit having the illustrated components, in addition to othercomponents.

FIG. 3 is a block diagram of a graphics processor 300, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores. In some embodiments,the graphics processor communicates via a memory mapped I/O interface toregisters on the graphics processor and with commands placed into theprocessor memory. In some embodiments, graphics processor 300 includes amemory interface 314 to access memory. Memory interface 314 can be aninterface to local memory, one or more internal caches, one or moreshared external caches, and/or to system memory.

In some embodiments, graphics processor 300 also includes a displaycontroller 302 to drive display output data to a display device 320.Display controller 302 includes hardware for one or more overlay planesfor the display and composition of multiple layers of video or userinterface elements. The display device 320 can be an internal orexternal display device. In one embodiment, the display device 320 is ahead mounted display device, such as a virtual reality (VR) displaydevice or an augmented reality (AR) display device. In some embodiments,graphics processor 300 includes a video codec engine 306 to encode,decode, or transcode media to, from, or between one or more mediaencoding formats, including, but not limited to Moving Picture ExpertsGroup (MPEG) formats such as MPEG-2, Advanced Video Coding (AVC) formatssuch as H.264/MPEG-4 AVC, as well as the Society of Motion Picture &Television Engineers (SMPTE) 421M/VC-1, and Joint Photographic ExpertsGroup (JPEG) formats such as JPEG, and Motion JPEG (MJPEG) formats.

In some embodiments, graphics processor 300 includes a block imagetransfer (BLIT) engine 304 to perform two-dimensional (2D) rasterizeroperations including, for example, bit-boundary block transfers.However, in one embodiment, 2D graphics operations are performed usingone or more components of graphics processing engine (GPE) 310. In someembodiments, GPE 310 is a compute engine for performing graphicsoperations, including three-dimensional (3D) graphics operations andmedia operations.

In some embodiments, GPE 310 includes a 3D pipeline 312 for performing3D operations, such as rendering three-dimensional images and scenesusing processing functions that act upon 3D primitive shapes (e.g.,rectangle, triangle, etc.). The 3D pipeline 312 includes programmableand fixed function elements that perform various tasks within theelement and/or spawn execution threads to a 3D/Media sub-system 315.While 3D pipeline 312 can be used to perform media operations, anembodiment of GPE 310 also includes a media pipeline 316 that isspecifically used to perform media operations, such as videopost-processing and image enhancement.

In some embodiments, media pipeline 316 includes fixed function orprogrammable logic units to perform one or more specialized mediaoperations, such as video decode acceleration, video de-interlacing, andvideo encode acceleration in place of, or on behalf of video codecengine 306. In some embodiments, media pipeline 316 additionallyincludes a thread spawning unit to spawn threads for execution on3D/Media sub-system 315. The spawned threads perform computations forthe media operations on one or more graphics execution units included in3D/Media sub-system 315.

In some embodiments, 3D/Media subsystem 315 includes logic for executingthreads spawned by 3D pipeline 312 and media pipeline 316. In oneembodiment, the pipelines send thread execution requests to 3D/Mediasubsystem 315, which includes thread dispatch logic for arbitrating anddispatching the various requests to available thread executionresources. The execution resources include an array of graphicsexecution units to process the 3D and media threads. In someembodiments, 3D/Media subsystem 315 includes one or more internal cachesfor thread instructions and data. In some embodiments, the subsystemalso includes shared memory, including registers and addressable memory,to share data between threads and to store output data.

Graphics Processing Engine

FIG. 4 is a block diagram of a graphics processing engine 410 of agraphics processor in accordance with some embodiments. In oneembodiment, the graphics processing engine (GPE) 410 is a version of theGPE 310 shown in FIG. 3. Elements of FIG. 4 having the same referencenumbers (or names) as the elements of any other figure herein canoperate or function in any manner similar to that described elsewhereherein, but are not limited to such. For example, the 3D pipeline 312and media pipeline 316 of FIG. 3 are illustrated. The media pipeline 316is optional in some embodiments of the GPE 410 and may not be explicitlyincluded within the GPE 410. For example, and in at least oneembodiment, a separate media and/or image processor is coupled to theGPE 410.

In some embodiments, GPE 410 couples with or includes a command streamer403, which provides a command stream to the 3D pipeline 312 and/or mediapipelines 316. In some embodiments, command streamer 403 is coupled withmemory, which can be system memory, or one or more of internal cachememory and shared cache memory. In some embodiments, command streamer403 receives commands from the memory and sends the commands to 3Dpipeline 312 and/or media pipeline 316. The commands are directivesfetched from a ring buffer, which stores commands for the 3D pipeline312 and media pipeline 316. In one embodiment, the ring buffer canadditionally include batch command buffers storing batches of multiplecommands. The commands for the 3D pipeline 312 can also includereferences to data stored in memory, such as but not limited to vertexand geometry data for the 3D pipeline 312 and/or image data and memoryobjects for the media pipeline 316. The 3D pipeline 312 and mediapipeline 316 process the commands and data by performing operations vialogic within the respective pipelines or by dispatching one or moreexecution threads to a graphics core array 414. In one embodiment, thegraphics core array 414 include one or more blocks of graphics cores(e.g., graphics core(s) 415A, graphics core(s) 415B), each blockincluding one or more graphics cores. Each graphics core includes a setof graphics execution resources that includes general-purpose andgraphics specific execution logic to perform graphics and computeoperations, as well as fixed function texture processing and/or machinelearning and artificial intelligence acceleration logic.

In various embodiments, the 3D pipeline 312 includes fixed function andprogrammable logic to process one or more shader programs, such asvertex shaders, geometry shaders, pixel shaders, fragment shaders,compute shaders, or other shader programs, by processing theinstructions and dispatching execution threads to the graphics corearray 414. The graphics core array 414 provides a unified block ofexecution resources for use in processing these shader programs.Multi-purpose execution logic (e.g., execution units) within thegraphics core(s) 415A-414B of the graphic core array 414 includessupport for various 3D API shader languages and can execute multiplesimultaneous execution threads associated with multiple shaders.

In some embodiments, the graphics core array 414 also includes executionlogic to perform media functions, such as video and/or image processing.In one embodiment, the execution units additionally includegeneral-purpose logic that is programmable to perform parallelgeneral-purpose computational operations, in addition to graphicsprocessing operations. The general-purpose logic can perform processingoperations in parallel or in conjunction with general-purpose logicwithin the processor core(s) 107 of FIG. 1 or core 202A-202N as in FIG.2.

Output data generated by threads executing on the graphics core array414 can output data to memory in a unified return buffer (URB) 418. TheURB 418 can store data for multiple threads. In some embodiments, theURB 418 may be used to send data between different threads executing onthe graphics core array 414. In some embodiments, the URB 418 mayadditionally be used for synchronization between threads on the graphicscore array and fixed function logic within the shared function logic420.

In some embodiments, graphics core array 414 is scalable, such that thearray includes a variable number of graphics cores, each having avariable number of execution units based on the target power andperformance level of GPE 410. In one embodiment, the execution resourcesare dynamically scalable, such that execution resources may be enabledor disabled as needed.

The graphics core array 414 couples with shared function logic 420 thatincludes multiple resources that are shared between the graphics coresin the graphics core array. The shared functions within the sharedfunction logic 420 are hardware logic units that provide specializedsupplemental functionality to the graphics core array 414. In variousembodiments, shared function logic 420 includes but is not limited tosampler 421, math 422, and inter-thread communication (ITC) 423 logic.Additionally, some embodiments implement one or more cache(s) 425 withinthe shared function logic 420.

A shared function is implemented where the demand for a givenspecialized function is insufficient for inclusion within the graphicscore array 414. Instead a single instantiation of that specializedfunction is implemented as a stand-alone entity in the shared functionlogic 420 and shared among the execution resources within the graphicscore array 414. The precise set of functions that are shared between thegraphics core array 414 and included within the graphics core array 414varies across embodiments. In some embodiments, specific sharedfunctions within the shared function logic 420 that are used extensivelyby the graphics core array 414 may be included within shared functionlogic 416 within the graphics core array 414. In various embodiments,the shared function logic 416 within the graphics core array 414 caninclude some or all logic within the shared function logic 420. In oneembodiment, all logic elements within the shared function logic 420 maybe duplicated within the shared function logic 416 of the graphics corearray 414. In one embodiment, the shared function logic 420 is excludedin favor of the shared function logic 416 within the graphics core array414.

FIG. 5 is a block diagram of hardware logic of a graphics processor core500, according to some embodiments described herein. Elements of FIG. 5having the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Theillustrated graphics processor core 500, in some embodiments, isincluded within the graphics core array 414 of FIG. 4. The graphicsprocessor core 500, sometimes referred to as a core slice, can be one ormultiple graphics cores within a modular graphics processor. Thegraphics processor core 500 is exemplary of one graphics core slice, anda graphics processor as described herein may include multiple graphicscore slices based on target power and performance envelopes. Eachgraphics processor core 500 can include a fixed function block 530coupled with multiple sub-cores 501A-501F, also referred to assub-slices, that include modular blocks of general-purpose and fixedfunction logic.

In some embodiments, the fixed function block 530 includes ageometry/fixed function pipeline 536 that can be shared by all sub-coresin the graphics processor core 500, for example, in lower performanceand/or lower power graphics processor implementations. In variousembodiments, the geometry/fixed function pipeline 536 includes a 3Dfixed function pipeline (e.g., 3D pipeline 312 as in FIG. 3 and FIG. 4)a video front-end unit, a thread spawner and thread dispatcher, and aunified return buffer manager, which manages unified return buffers,such as the unified return buffer 418 of FIG. 4.

In one embodiment, the fixed function block 530 also includes a graphicsSoC interface 537, a graphics microcontroller 538, and a media pipeline539. The graphics SoC interface 537 provides an interface between thegraphics processor core 500 and other processor cores within a system ona chip integrated circuit. The graphics microcontroller 538 is aprogrammable sub-processor that is configurable to manage variousfunctions of the graphics processor core 500, including thread dispatch,scheduling, and preemption. The media pipeline 539 (e.g., media pipeline316 of FIG. 3 and FIG. 4) includes logic to facilitate the decoding,encoding, pre-processing, and/or post-processing of multimedia data,including image and video data. The media pipeline 539 implement mediaoperations via requests to compute or sampling logic within thesub-cores 501-501F.

In one embodiment, the SoC interface 537 enables the graphics processorcore 500 to communicate with general-purpose application processor cores(e.g., CPUs) and/or other components within an SoC, including memoryhierarchy elements such as a shared last level cache memory, the systemRAM, and/or embedded on-chip or on-package DRAM. The SoC interface 537can also enable communication with fixed function devices within theSoC, such as camera imaging pipelines, and enables the use of and/orimplements global memory atomics that may be shared between the graphicsprocessor core 500 and CPUs within the SoC. The SoC interface 537 canalso implement power management controls for the graphics processor core500 and enable an interface between a clock domain of the graphicprocessor core 500 and other clock domains within the SoC. In oneembodiment, the SoC interface 537 enables receipt of command buffersfrom a command streamer and global thread dispatcher that are configuredto provide commands and instructions to each of one or more graphicscores within a graphics processor. The commands and instructions can bedispatched to the media pipeline 539, when media operations are to beperformed, or a geometry and fixed function pipeline (e.g., geometry andfixed function pipeline 536, geometry and fixed function pipeline 514)when graphics processing operations are to be performed.

The graphics microcontroller 538 can be configured to perform variousscheduling and management tasks for the graphics processor core 500. Inone embodiment, the graphics microcontroller 538 can perform graphicsand/or compute workload scheduling on the various graphics parallelengines within execution unit (EU) arrays 502A-502F, 504A-504F withinthe sub-cores 501A-501F. In this scheduling model, host softwareexecuting on a CPU core of an SoC including the graphics processor core500 can submit workloads one of multiple graphic processor doorbells,which invokes a scheduling operation on the appropriate graphics engine.Scheduling operations include determining which workload to run next,submitting a workload to a command streamer, pre-empting existingworkloads running on an engine, monitoring progress of a workload, andnotifying host software when a workload is complete. In one embodiment,the graphics microcontroller 538 can also facilitate low-power or idlestates for the graphics processor core 500, providing the graphicsprocessor core 500 with the ability to save and restore registers withinthe graphics processor core 500 across low-power state transitionsindependently from the operating system and/or graphics driver softwareon the system.

The graphics processor core 500 may have greater than or fewer than theillustrated sub-cores 501A-501F, up to N modular sub-cores. For each setof N sub-cores, the graphics processor core 500 can also include sharedfunction logic 510, shared and/or cache memory 512, a geometry/fixedfunction pipeline 514, as well as additional fixed function logic 516 toaccelerate various graphics and compute processing operations. Theshared function logic 510 can include logic units associated with theshared function logic 420 of FIG. 4 (e.g., sampler, math, and/orinter-thread communication logic) that can be shared by each N sub-coreswithin the graphics processor core 500. The shared and/or cache memory512 can be a last-level cache for the set of N sub-cores 501A-501Fwithin the graphics processor core 500, and can also serve as sharedmemory that is accessible by multiple sub-cores. The geometry/fixedfunction pipeline 514 can be included instead of the geometry/fixedfunction pipeline 536 within the fixed function block 530 and caninclude the same or similar logic units.

In one embodiment, the graphics processor core 500 includes additionalfixed function logic 516 that can include various fixed functionacceleration logic for use by the graphics processor core 500. In oneembodiment, the additional fixed function logic 516 includes anadditional geometry pipeline for use in position only shading. Inposition-only shading, two geometry pipelines exist, the full geometrypipeline within the geometry/fixed function pipeline 516, 536, and acull pipeline, which is an additional geometry pipeline which may beincluded within the additional fixed function logic 516. In oneembodiment, the cull pipeline is a trimmed down version of the fullgeometry pipeline. The full pipeline and the cull pipeline can executedifferent instances of the same application, each instance having aseparate context. Position only shading can hide long cull runs ofdiscarded triangles, enabling shading to be completed earlier in someinstances. For example, and in one embodiment the cull pipeline logicwithin the additional fixed function logic 516 can execute positionshaders in parallel with the main application and generally generatescritical results faster than the full pipeline, as the cull pipelinefetches and shades only the position attribute of the vertices, withoutperforming rasterization and rendering of the pixels to the framebuffer. The cull pipeline can use the generated critical results tocompute visibility information for all the triangles without regard towhether those triangles are culled. The full pipeline (which in thisinstance may be referred to as a replay pipeline) can consume thevisibility information to skip the culled triangles to shade only thevisible triangles that are finally passed to the rasterization phase.

In one embodiment, the additional fixed function logic 516 can alsoinclude machine-learning acceleration logic, such as fixed functionmatrix multiplication logic, for implementations including optimizationsfor machine learning training or inferencing.

Within each graphics sub-core 501A-501F includes a set of executionresources that may be used to perform graphics, media, and computeoperations in response to requests by graphics pipeline, media pipeline,or shader programs. The graphics sub-cores 501A-501F include multiple EUarrays 502A-502F, 504A-504F, thread dispatch and inter-threadcommunication (TD/IC) logic 503A-503F, a 3D (e.g., texture) sampler505A-505F, a media sampler 506A-506F, a shader processor 507A-507F, andshared local memory (SLM) 508A-508F. The EU arrays 502A-502F, 504A-504Feach include multiple execution units, which are general-purposegraphics processing units capable of performing floating-point andinteger/fixed-point logic operations in service of a graphics, media, orcompute operation, including graphics, media, or compute shaderprograms. The TD/IC logic 503A-503F performs local thread dispatch andthread control operations for the execution units within a sub-core andfacilitate communication between threads executing on the executionunits of the sub-core. The 3D sampler 505A-505F can read texture orother 3D graphics related data into memory. The 3D sampler can readtexture data differently based on a configured sample state and thetexture format associated with a given texture. The media sampler506A-506F can perform similar read operations based on the type andformat associated with media data. In one embodiment, each graphicssub-core 501A-501F can alternately include a unified 3D and mediasampler. Threads executing on the execution units within each of thesub-cores 501A-501F can make use of shared local memory 508A-508F withineach sub-core, to enable threads executing within a thread group toexecute using a common pool of on-chip memory.

Execution Units

FIGS. 6A-6B illustrate thread execution logic 600 including an array ofprocessing elements employed in a graphics processor core according toembodiments described herein. Elements of FIGS. 6A-6B having the samereference numbers (or names) as the elements of any other figure hereincan operate or function in any manner similar to that describedelsewhere herein, but are not limited to such. FIG. 6A illustrates anoverview of thread execution logic 600, which can include a variant ofthe hardware logic illustrated with each sub-core 501A-501F of FIG. 5.FIG. 6B illustrates exemplary internal details of an execution unit.

As illustrated in FIG. 6A, in some embodiments thread execution logic600 includes a shader processor 602, a thread dispatcher 604,instruction cache 606, a scalable execution unit array including aplurality of execution units 608A-608N, a sampler 610, a data cache 612,and a data port 614. In one embodiment, the scalable execution unitarray can dynamically scale by enabling or disabling one or moreexecution units (e.g., any of execution unit 608A, 608B, 608C, 608D,through 608N-1 and 608N) based on the computational requirements of aworkload. In one embodiment, the included components are interconnectedvia an interconnect fabric that links to each of the components. In someembodiments, thread execution logic 600 includes one or more connectionsto memory, such as system memory or cache memory, through one or more ofinstruction cache 606, data port 614, sampler 610, and execution units608A-608N. In some embodiments, each execution unit (e.g. 608A) is astand-alone programmable general-purpose computational unit that iscapable of executing multiple simultaneous hardware threads whileprocessing multiple data elements in parallel for each thread. Invarious embodiments, the array of execution units 608A-608N is scalableto include any number individual execution units.

In some embodiments, the execution units 608A-608N are primarily used toexecute shader programs. A shader processor 602 can process the variousshader programs and dispatch execution threads associated with theshader programs via a thread dispatcher 604. In one embodiment, thethread dispatcher includes logic to arbitrate thread initiation requestsfrom the graphics and media pipelines and instantiate the requestedthreads on one or more execution unit in the execution units 608A-608N.For example, a geometry pipeline can dispatch vertex, tessellation, orgeometry shaders to the thread execution logic for processing. In someembodiments, thread dispatcher 604 can also process runtime threadspawning requests from the executing shader programs.

In some embodiments, the execution units 608A-608N support aninstruction set that includes native support for many standard 3Dgraphics shader instructions, such that shader programs from graphicslibraries (e.g., Direct 3D and OpenGL) are executed with a minimaltranslation. The execution units support vertex and geometry processing(e.g., vertex programs, geometry programs, vertex shaders), pixelprocessing (e.g., pixel shaders, fragment shaders) and general-purposeprocessing (e.g., compute and media shaders). Each of the executionunits 608A-608N is capable of multi-issue single instruction multipledata (SIMD) execution and multi-threaded operation enables an efficientexecution environment in the face of higher latency memory accesses.Each hardware thread within each execution unit has a dedicatedhigh-bandwidth register file and associated independent thread-state.Execution is multi-issue per clock to pipelines capable of integer,single and double precision floating point operations, SIMD branchcapability, logical operations, transcendental operations, and othermiscellaneous operations. While waiting for data from memory or one ofthe shared functions, dependency logic within the execution units608A-608N causes a waiting thread to sleep until the requested data hasbeen returned. While the waiting thread is sleeping, hardware resourcesmay be devoted to processing other threads. For example, during a delayassociated with a vertex shader operation, an execution unit can performoperations for a pixel shader, fragment shader, or another type ofshader program, including a different vertex shader.

Each execution unit in execution units 608A-608N operates on arrays ofdata elements. The number of data elements is the “execution size,” orthe number of channels for the instruction. An execution channel is alogical unit of execution for data element access, masking, and flowcontrol within instructions. The number of channels may be independentof the number of physical Arithmetic Logic Units (ALUs) or FloatingPoint Units (FPUs) for a particular graphics processor. In someembodiments, execution units 608A-608N support integer andfloating-point data types.

The execution unit instruction set includes SIMD instructions. Thevarious data elements can be stored as a packed data type in a registerand the execution unit will process the various elements based on thedata size of the elements. For example, when operating on a 256-bit widevector, the 256 bits of the vector are stored in a register and theexecution unit operates on the vector as four separate 64-bit packeddata elements (Quad-Word (QW) size data elements), eight separate 32-bitpacked data elements (Double Word (DW) size data elements), sixteenseparate 16-bit packed data elements (Word (W) size data elements), orthirty-two separate 8-bit data elements (byte (B) size data elements).However, different vector widths and register sizes are possible.

In one embodiment one or more execution units can be combined into afused execution unit 609A-609N having thread control logic (607A-607N)that is common to the fused EUs. Multiple EUs can be fused into an EUgroup. Each EU in the fused EU group can be configured to execute aseparate SIMD hardware thread. The number of EUs in a fused EU group canvary according to embodiments. Additionally, various SIMD widths can beperformed per-EU, including but not limited to SIMD8, SIMD16, andSIMD32. Each fused graphics execution unit 609A-609N includes at leasttwo execution units. For example, fused execution unit 609A includes afirst EU 608A, second EU 608B, and thread control logic 607A that iscommon to the first EU 608A and the second EU 608B. The thread controllogic 607A controls threads executed on the fused graphics executionunit 609A, allowing each EU within the fused execution units 609A-609Nto execute using a common instruction pointer register.

One or more internal instruction caches (e.g., 606) are included in thethread execution logic 600 to cache thread instructions for theexecution units. In some embodiments, one or more data caches (e.g.,612) are included to cache thread data during thread execution. In someembodiments, a sampler 610 is included to provide texture sampling for3D operations and media sampling for media operations. In someembodiments, sampler 610 includes specialized texture or media samplingfunctionality to process texture or media data during the samplingprocess before providing the sampled data to an execution unit.

During execution, the graphics and media pipelines send threadinitiation requests to thread execution logic 600 via thread spawningand dispatch logic. Once a group of geometric objects has been processedand rasterized into pixel data, pixel processor logic (e.g., pixelshader logic, fragment shader logic, etc.) within the shader processor602 is invoked to further compute output information and cause resultsto be written to output surfaces (e.g., color buffers, depth buffers,stencil buffers, etc.). In some embodiments, a pixel shader or fragmentshader calculates the values of the various vertex attributes that areto be interpolated across the rasterized object. In some embodiments,pixel processor logic within the shader processor 602 then executes anapplication programming interface (API)-supplied pixel or fragmentshader program. To execute the shader program, the shader processor 602dispatches threads to an execution unit (e.g., 608A) via threaddispatcher 604. In some embodiments, shader processor 602 uses texturesampling logic in the sampler 610 to access texture data in texture mapsstored in memory. Arithmetic operations on the texture data and theinput geometry data compute pixel color data for each geometricfragment, or discards one or more pixels from further processing.

In some embodiments, the data port 614 provides a memory accessmechanism for the thread execution logic 600 to output processed data tomemory for further processing on a graphics processor output pipeline.In some embodiments, the data port 614 includes or couples to one ormore cache memories (e.g., data cache 612) to cache data for memoryaccess via the data port.

As illustrated in FIG. 6B, a graphics execution unit 608 can include aninstruction fetch unit 637, a general register file array (GRF) 624, anarchitectural register file array (ARF) 626, a thread arbiter 622, asend unit 630, a branch unit 632, a set of SIMD floating point units(FPUs) 634, and in one embodiment a set of dedicated integer SIMD ALUs635. The GRF 624 and ARF 626 includes the set of general register filesand architecture register files associated with each simultaneoushardware thread that may be active in the graphics execution unit 608.In one embodiment, per thread architectural state is maintained in theARF 626, while data used during thread execution is stored in the GRF624. The execution state of each thread, including the instructionpointers for each thread, can be held in thread-specific registers inthe ARF 626.

In one embodiment, the graphics execution unit 608 has an architecturethat is a combination of Simultaneous Multi-Threading (SMT) andfine-grained Interleaved Multi-Threading (IMT). The architecture has amodular configuration that can be fine-tuned at design time based on atarget number of simultaneous threads and number of registers perexecution unit, where execution unit resources are divided across logicused to execute multiple simultaneous threads.

In one embodiment, the graphics execution unit 608 can co-issue multipleinstructions, which may each be different instructions. The threadarbiter 622 of the graphics execution unit thread 608 can dispatch theinstructions to one of the send unit 630, branch unit 642, or SIMDFPU(s) 634 for execution. Each execution thread can access 128general-purpose registers within the GRF 624, where each register canstore 32 bytes, accessible as a SIMD 8-element vector of 32-bit dataelements. In one embodiment, each execution unit thread has access to 4Kbytes within the GRF 624, although embodiments are not so limited, andgreater or fewer register resources may be provided in otherembodiments. In one embodiment, up to seven threads can executesimultaneously, although the number of threads per execution unit canalso vary according to embodiments. In an embodiment in which seventhreads may access 4 Kbytes, the GRF 624 can store a total of 28 Kbytes.Flexible addressing modes can permit registers to be addressed togetherto build effectively wider registers or to represent strided rectangularblock data structures.

In one embodiment, memory operations, sampler operations, and otherlonger-latency system communications are dispatched via “send”instructions that are executed by the message passing send unit 630. Inone embodiment, branch instructions are dispatched to a dedicated branchunit 632 to facilitate SIMD divergence and eventual convergence.

In one embodiment, the graphics execution unit 608 includes one or moreSIMD floating point units (FPU(s)) 634 to perform floating-pointoperations. In one embodiment, the FPU(s) 634 also support integercomputation. In one embodiment, the FPU(s) 634 can SIMD execute up to Mnumber of 32-bit floating-point (or integer) operations, or SIMD executeup to 2M 16-bit integer or 16-bit floating-point operations. In oneembodiment, at least one of the FPU(s) provides extended math capabilityto support high-throughput transcendental math functions and doubleprecision 64-bit floating-point. In some embodiments, a set of 8-bitinteger SIMD ALUs 635 are also present, and may be specificallyoptimized to perform operations associated with machine learningcomputations.

In one embodiment, arrays of multiple instances of the graphicsexecution unit 608 can be instantiated in a graphics sub-core grouping(e.g., a sub-slice). For scalability, product architects can choose theexact number of execution units per sub-core grouping. In oneembodiment, the execution unit 608 can execute instructions across aplurality of execution channels. In a further embodiment, each threadexecuted on the graphics execution unit 608 is executed on a differentchannel.

FIG. 7 is a block diagram illustrating a graphics processor instructionformats 700 according to some embodiments. In one or more embodiment,the graphics processor execution units support an instruction set havinginstructions in multiple formats. The solid lined boxes illustrate thecomponents that are generally included in an execution unit instruction,while the dashed lines include components that are optional or that areonly included in a sub-set of the instructions. In some embodiments,instruction format 700 described and illustrated are macro-instructions,in that they are instructions supplied to the execution unit, as opposedto micro-operations resulting from instruction decode once theinstruction is processed.

In some embodiments, the graphics processor execution units nativelysupport instructions in a 128-bit instruction format 710. A 64-bitcompacted instruction format 730 is available for some instructionsbased on the selected instruction, instruction options, and number ofoperands. The native 128-bit instruction format 710 provides access toall instruction options, while some options and operations arerestricted in the 64-bit format 730. The native instructions availablein the 64-bit format 730 vary by embodiment. In some embodiments, theinstruction is compacted in part using a set of index values in an indexfield 713. The execution unit hardware references a set of compactiontables based on the index values and uses the compaction table outputsto reconstruct a native instruction in the 128-bit instruction format710.

For each format, instruction opcode 712 defines the operation that theexecution unit is to perform. The execution units execute eachinstruction in parallel across the multiple data elements of eachoperand. For example, in response to an add instruction the executionunit performs a simultaneous add operation across each color channelrepresenting a texture element or picture element. By default, theexecution unit performs each instruction across all data channels of theoperands. In some embodiments, instruction control field 714 enablescontrol over certain execution options, such as channels selection(e.g., predication) and data channel order (e.g., swizzle). Forinstructions in the 128-bit instruction format 710 an exec-size field716 limits the number of data channels that will be executed inparallel. In some embodiments, exec-size field 716 is not available foruse in the 64-bit compact instruction format 730.

Some execution unit instructions have up to three operands including twosource operands, src0 720, src1 722, and one destination 718. In someembodiments, the execution units support dual destination instructions,where one of the destinations is implied. Data manipulation instructionscan have a third source operand (e.g., SRC2 724), where the instructionopcode 712 determines the number of source operands. An instruction'slast source operand can be an immediate (e.g., hard-coded) value passedwith the instruction.

In some embodiments, the 128-bit instruction format 710 includes anaccess/address mode field 726 specifying, for example, whether directregister addressing mode or indirect register addressing mode is used.When direct register addressing mode is used, the register address ofone or more operands is directly provided by bits in the instruction.

In some embodiments, the 128-bit instruction format 710 includes anaccess/address mode field 726, which specifies an address mode and/or anaccess mode for the instruction. In one embodiment, the access mode isused to define a data access alignment for the instruction. Someembodiments support access modes including a 16-byte aligned access modeand a 1-byte aligned access mode, where the byte alignment of the accessmode determines the access alignment of the instruction operands. Forexample, when in a first mode, the instruction may use byte-alignedaddressing for source and destination operands and when in a secondmode, the instruction may use 16-byte-aligned addressing for all sourceand destination operands.

In one embodiment, the address mode portion of the access/address modefield 726 determines whether the instruction is to use direct orindirect addressing. When direct register addressing mode is used bitsin the instruction directly provide the register address of one or moreoperands. When indirect register addressing mode is used, the registeraddress of one or more operands may be computed based on an addressregister value and an address immediate field in the instruction.

In some embodiments instructions are grouped based on opcode 712bit-fields to simplify Opcode decode 740. For an 8-bit opcode, bits 4,5, and 6 allow the execution unit to determine the type of opcode. Theprecise opcode grouping shown is merely an example. In some embodiments,a move and logic opcode group 742 includes data movement and logicinstructions (e.g., move (mov), compare (cmp)). In some embodiments,move and logic group 742 shares the five most significant bits (MSB),where move (mov) instructions are in the form of 0000xxxxb and logicinstructions are in the form of 0001xxxxb. A flow control instructiongroup 744 (e.g., call, jump (jmp)) includes instructions in the form of0010xxxxb (e.g., 0x20). A miscellaneous instruction group 746 includes amix of instructions, including synchronization instructions (e.g., wait,send) in the form of 0011xxxxb (e.g., 0x30). A parallel math instructiongroup 748 includes component-wise arithmetic instructions (e.g., add,multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). The parallel mathgroup 748 performs the arithmetic operations in parallel across datachannels. The vector math group 750 includes arithmetic instructions(e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). The vector math groupperforms arithmetic such as dot product calculations on vector operands.

Graphics Pipeline

FIG. 8 is a block diagram of another embodiment of a graphics processor800. Elements of FIG. 8 having the same reference numbers (or names) asthe elements of any other figure herein can operate or function in anymanner similar to that described elsewhere herein, but are not limitedto such.

In some embodiments, graphics processor 800 includes a geometry pipeline820, a media pipeline 830, a display engine 840, thread execution logic850, and a render output pipeline 870. In some embodiments, graphicsprocessor 800 is a graphics processor within a multi-core processingsystem that includes one or more general-purpose processing cores. Thegraphics processor is controlled by register writes to one or morecontrol registers (not shown) or via commands issued to graphicsprocessor 800 via a ring interconnect 802. In some embodiments, ringinterconnect 802 couples graphics processor 800 to other processingcomponents, such as other graphics processors or general-purposeprocessors. Commands from ring interconnect 802 are interpreted by acommand streamer 803, which supplies instructions to individualcomponents of the geometry pipeline 820 or the media pipeline 830.

In some embodiments, command streamer 803 directs the operation of avertex fetcher 805 that reads vertex data from memory and executesvertex-processing commands provided by command streamer 803. In someembodiments, vertex fetcher 805 provides vertex data to a vertex shader807, which performs coordinate space transformation and lightingoperations to each vertex. In some embodiments, vertex fetcher 805 andvertex shader 807 execute vertex-processing instructions by dispatchingexecution threads to execution units 852A-852B via a thread dispatcher831.

In some embodiments, execution units 852A-852B are an array of vectorprocessors having an instruction set for performing graphics and mediaoperations. In some embodiments, execution units 852A-852B have anattached L1 cache 851 that is specific for each array or shared betweenthe arrays. The cache can be configured as a data cache, an instructioncache, or a single cache that is partitioned to contain data andinstructions in different partitions.

In some embodiments, geometry pipeline 820 includes tessellationcomponents to perform hardware-accelerated tessellation of 3D objects.In some embodiments, a programmable hull shader 811 configures thetessellation operations. A programmable domain shader 817 providesback-end evaluation of tessellation output. A tessellator 813 operatesat the direction of hull shader 811 and contains special purpose logicto generate a set of detailed geometric objects based on a coarsegeometric model that is provided as input to geometry pipeline 820. Insome embodiments, if tessellation is not used, tessellation components(e.g., hull shader 811, tessellator 813, and domain shader 817) can bebypassed.

In some embodiments, complete geometric objects can be processed by ageometry shader 819 via one or more threads dispatched to executionunits 852A-852B, or can proceed directly to the clipper 829. In someembodiments, the geometry shader operates on entire geometric objects,rather than vertices or patches of vertices as in previous stages of thegraphics pipeline. If the tessellation is disabled the geometry shader819 receives input from the vertex shader 807. In some embodiments,geometry shader 819 is programmable by a geometry shader program toperform geometry tessellation if the tessellation units are disabled.

Before rasterization, clipper 829 processes vertex data. The clipper 829may be a fixed function clipper or a programmable clipper havingclipping and geometry shader functions. In some embodiments, arasterizer and depth test component 873 in the render output pipeline870 dispatches pixel shaders to convert the geometric objects into perpixel representations. In some embodiments, pixel shader logic isincluded in thread execution logic 850. In some embodiments, anapplication can bypass the rasterizer and depth test component 873 andaccess un-rasterized vertex data via a stream out unit 823.

The graphics processor 800 has an interconnect bus, interconnect fabric,or some other interconnect mechanism that allows data and messagepassing amongst the major components of the processor. In someembodiments, execution units 852A-852B and associated logic units (e.g.,L1 cache 851, sampler 854, texture cache 858, etc.) interconnect via adata port 856 to perform memory access and communicate with renderoutput pipeline components of the processor. In some embodiments,sampler 854, caches 851, 858 and execution units 852A-852B each haveseparate memory access paths. In one embodiment, the texture cache 858can also be configured as a sampler cache.

In some embodiments, render output pipeline 870 contains a rasterizerand depth test component 873 that converts vertex-based objects into anassociated pixel-based representation. In some embodiments, therasterizer logic includes a windower/masker unit to perform fixedfunction triangle and line rasterization. An associated render cache 878and depth cache 879 are also available in some embodiments. A pixeloperations component 877 performs pixel-based operations on the data,though in some instances, pixel operations associated with 2D operations(e.g. bit block image transfers with blending) are performed by the 2Dengine 841, or substituted at display time by the display controller 843using overlay display planes. In some embodiments, a shared L3 cache 875is available to all graphics components, allowing the sharing of datawithout the use of main system memory.

In some embodiments, graphics processor media pipeline 830 includes amedia engine 837 and a video front-end 834. In some embodiments, videofront-end 834 receives pipeline commands from the command streamer 803.In some embodiments, media pipeline 830 includes a separate commandstreamer. In some embodiments, video front-end 834 processes mediacommands before sending the command to the media engine 837. In someembodiments, media engine 837 includes thread spawning functionality tospawn threads for dispatch to thread execution logic 850 via threaddispatcher 831.

In some embodiments, graphics processor 800 includes a display engine840. In some embodiments, display engine 840 is external to processor800 and couples with the graphics processor via the ring interconnect802, or some other interconnect bus or fabric. In some embodiments,display engine 840 includes a 2D engine 841 and a display controller843. In some embodiments, display engine 840 contains special purposelogic capable of operating independently of the 3D pipeline. In someembodiments, display controller 843 couples with a display device (notshown), which may be a system integrated display device, as in a laptopcomputer, or an external display device attached via a display deviceconnector.

In some embodiments, the geometry pipeline 820 and media pipeline 830are configurable to perform operations based on multiple graphics andmedia programming interfaces and are not specific to any one applicationprogramming interface (API). In some embodiments, driver software forthe graphics processor translates API calls that are specific to aparticular graphics or media library into commands that can be processedby the graphics processor. In some embodiments, support is provided forthe Open Graphics Library (OpenGL), Open Computing Language (OpenCL),and/or Vulkan graphics and compute API, all from the Khronos Group. Insome embodiments, support may also be provided for the Direct3D libraryfrom the Microsoft Corporation. In some embodiments, a combination ofthese libraries may be supported. Support may also be provided for theOpen Source Computer Vision Library (OpenCV). A future API with acompatible 3D pipeline would also be supported if a mapping can be madefrom the pipeline of the future API to the pipeline of the graphicsprocessor.

Graphics Pipeline Programming

FIG. 9A is a block diagram illustrating a graphics processor commandformat 900 according to some embodiments. FIG. 9B is a block diagramillustrating a graphics processor command sequence 910 according to anembodiment. The solid lined boxes in FIG. 9A illustrate the componentsthat are generally included in a graphics command while the dashed linesinclude components that are optional or that are only included in asub-set of the graphics commands. The exemplary graphics processorcommand format 900 of FIG. 9A includes data fields to identify a client902, a command operation code (opcode) 904, and data 906 for thecommand. A sub-opcode 905 and a command size 908 are also included insome commands.

In some embodiments, client 902 specifies the client unit of thegraphics device that processes the command data. In some embodiments, agraphics processor command parser examines the client field of eachcommand to condition the further processing of the command and route thecommand data to the appropriate client unit. In some embodiments, thegraphics processor client units include a memory interface unit, arender unit, a 2D unit, a 3D unit, and a media unit. Each client unithas a corresponding processing pipeline that processes the commands.Once the command is received by the client unit, the client unit readsthe opcode 904 and, if present, sub-opcode 905 to determine theoperation to perform. The client unit performs the command usinginformation in data field 906. For some commands an explicit commandsize 908 is expected to specify the size of the command. In someembodiments, the command parser automatically determines the size of atleast some of the commands based on the command opcode. In someembodiments commands are aligned via multiples of a double word.

The flow diagram in FIG. 9B illustrates an exemplary graphics processorcommand sequence 910. In some embodiments, software or firmware of adata processing system that features an embodiment of a graphicsprocessor uses a version of the command sequence shown to set up,execute, and terminate a set of graphics operations. A sample commandsequence is shown and described for purposes of example only asembodiments are not limited to these specific commands or to thiscommand sequence. Moreover, the commands may be issued as batch ofcommands in a command sequence, such that the graphics processor willprocess the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 910 maybegin with a pipeline flush command 912 to cause any active graphicspipeline to complete the currently pending commands for the pipeline. Insome embodiments, the 3D pipeline 922 and the media pipeline 924 do notoperate concurrently. The pipeline flush is performed to cause theactive graphics pipeline to complete any pending commands. In responseto a pipeline flush, the command parser for the graphics processor willpause command processing until the active drawing engines completepending operations and the relevant read caches are invalidated.Optionally, any data in the render cache that is marked ‘dirty’ can beflushed to memory. In some embodiments, pipeline flush command 912 canbe used for pipeline synchronization or before placing the graphicsprocessor into a low power state.

In some embodiments, a pipeline select command 913 is used when acommand sequence requires the graphics processor to explicitly switchbetween pipelines. In some embodiments, a pipeline select command 913 isrequired only once within an execution context before issuing pipelinecommands unless the context is to issue commands for both pipelines. Insome embodiments, a pipeline flush command 912 is required immediatelybefore a pipeline switch via the pipeline select command 913.

In some embodiments, a pipeline control command 914 configures agraphics pipeline for operation and is used to program the 3D pipeline922 and the media pipeline 924. In some embodiments, pipeline controlcommand 914 configures the pipeline state for the active pipeline. Inone embodiment, the pipeline control command 914 is used for pipelinesynchronization and to clear data from one or more cache memories withinthe active pipeline before processing a batch of commands.

In some embodiments, return buffer state commands 916 are used toconfigure a set of return buffers for the respective pipelines to writedata. Some pipeline operations require the allocation, selection, orconfiguration of one or more return buffers into which the operationswrite intermediate data during processing. In some embodiments, thegraphics processor also uses one or more return buffers to store outputdata and to perform cross thread communication. In some embodiments, thereturn buffer state 916 includes selecting the size and number of returnbuffers to use for a set of pipeline operations.

The remaining commands in the command sequence differ based on theactive pipeline for operations. Based on a pipeline determination 920,the command sequence is tailored to the 3D pipeline 922 beginning withthe 3D pipeline state 930 or the media pipeline 924 beginning at themedia pipeline state 940.

The commands to configure the 3D pipeline state 930 include 3D statesetting commands for vertex buffer state, vertex element state, constantcolor state, depth buffer state, and other state variables that are tobe configured before 3D primitive commands are processed. The values ofthese commands are determined at least in part based on the particular3D API in use. In some embodiments, 3D pipeline state 930 commands arealso able to selectively disable or bypass certain pipeline elements ifthose elements will not be used.

In some embodiments, 3D primitive 932 command is used to submit 3Dprimitives to be processed by the 3D pipeline. Commands and associatedparameters that are passed to the graphics processor via the 3Dprimitive 932 command are forwarded to the vertex fetch function in thegraphics pipeline. The vertex fetch function uses the 3D primitive 932command data to generate vertex data structures. The vertex datastructures are stored in one or more return buffers. In someembodiments, 3D primitive 932 command is used to perform vertexoperations on 3D primitives via vertex shaders. To process vertexshaders, 3D pipeline 922 dispatches shader execution threads to graphicsprocessor execution units.

In some embodiments, 3D pipeline 922 is triggered via an execute 934command or event. In some embodiments, a register write triggers commandexecution. In some embodiments execution is triggered via a ‘go’ or‘kick’ command in the command sequence. In one embodiment, commandexecution is triggered using a pipeline synchronization command to flushthe command sequence through the graphics pipeline. The 3D pipeline willperform geometry processing for the 3D primitives. Once operations arecomplete, the resulting geometric objects are rasterized and the pixelengine colors the resulting pixels. Additional commands to control pixelshading and pixel back end operations may also be included for thoseoperations.

In some embodiments, the graphics processor command sequence 910 followsthe media pipeline 924 path when performing media operations. Ingeneral, the specific use and manner of programming for the mediapipeline 924 depends on the media or compute operations to be performed.Specific media decode operations may be offloaded to the media pipelineduring media decode. In some embodiments, the media pipeline can also bebypassed and media decode can be performed in whole or in part usingresources provided by one or more general-purpose processing cores. Inone embodiment, the media pipeline also includes elements forgeneral-purpose graphics processor unit (GPGPU) operations, where thegraphics processor is used to perform SIMD vector operations usingcomputational shader programs that are not explicitly related to therendering of graphics primitives.

In some embodiments, media pipeline 924 is configured in a similarmanner as the 3D pipeline 922. A set of commands to configure the mediapipeline state 940 are dispatched or placed into a command queue beforethe media object commands 942. In some embodiments, commands for themedia pipeline state 940 include data to configure the media pipelineelements that will be used to process the media objects. This includesdata to configure the video decode and video encode logic within themedia pipeline, such as encode or decode format. In some embodiments,commands for the media pipeline state 940 also support the use of one ormore pointers to “indirect” state elements that contain a batch of statesettings.

In some embodiments, media object commands 942 supply pointers to mediaobjects for processing by the media pipeline. The media objects includememory buffers containing video data to be processed. In someembodiments, all media pipeline states must be valid before issuing amedia object command 942. Once the pipeline state is configured andmedia object commands 942 are queued, the media pipeline 924 istriggered via an execute command 944 or an equivalent execute event(e.g., register write). Output from media pipeline 924 may then be postprocessed by operations provided by the 3D pipeline 922 or the mediapipeline 924. In some embodiments, GPGPU operations are configured andexecuted in a similar manner as media operations.

Graphics Software Architecture

FIG. 10 illustrates exemplary graphics software architecture for a dataprocessing system 1000 according to some embodiments. In someembodiments, software architecture includes a 3D graphics application1010, an operating system 1020, and at least one processor 1030. In someembodiments, processor 1030 includes a graphics processor 1032 and oneor more general-purpose processor core(s) 1034. The graphics application1010 and operating system 1020 each execute in the system memory 1050 ofthe data processing system.

In some embodiments, 3D graphics application 1010 contains one or moreshader programs including shader instructions 1012. The shader languageinstructions may be in a high-level shader language, such as the HighLevel Shader Language (HLSL) or the OpenGL Shader Language (GLSL). Theapplication also includes executable instructions 1014 in a machinelanguage suitable for execution by the general-purpose processor core1034. The application also includes graphics objects 1016 defined byvertex data.

In some embodiments, operating system 1020 is a Microsoft® Windows®operating system from the Microsoft Corporation, a proprietary UNIX-likeoperating system, or an open source UNIX-like operating system using avariant of the Linux kernel. The operating system 1020 can support agraphics API 1022 such as the Direct3D API, the OpenGL API, or theVulkan API. When the Direct3D API is in use, the operating system 1020uses a front-end shader compiler 1024 to compile any shader instructions1012 in HLSL into a lower-level shader language. The compilation may bea just-in-time (JIT) compilation or the application can perform shaderpre-compilation. In some embodiments, high-level shaders are compiledinto low-level shaders during the compilation of the 3D graphicsapplication 1010. In some embodiments, the shader instructions 1012 areprovided in an intermediate form, such as a version of the StandardPortable Intermediate Representation (SPIR) used by the Vulkan API.

In some embodiments, user mode graphics driver 1026 contains a back-endshader compiler 1027 to convert the shader instructions 1012 into ahardware specific representation. When the OpenGL API is in use, shaderinstructions 1012 in the GLSL high-level language are passed to a usermode graphics driver 1026 for compilation. In some embodiments, usermode graphics driver 1026 uses operating system kernel mode functions1028 to communicate with a kernel mode graphics driver 1029. In someembodiments, kernel mode graphics driver 1029 communicates with graphicsprocessor 1032 to dispatch commands and instructions.

IP Core Implementations

One or more aspects of at least one embodiment may be implemented byrepresentative code stored on a machine-readable medium which representsand/or defines logic within an integrated circuit such as a processor.For example, the machine-readable medium may include instructions whichrepresent various logic within the processor. When read by a machine,the instructions may cause the machine to fabricate the logic to performthe techniques described herein. Such representations, known as “IPcores,” are reusable units of logic for an integrated circuit that maybe stored on a tangible, machine-readable medium as a hardware modelthat describes the structure of the integrated circuit. The hardwaremodel may be supplied to various customers or manufacturing facilities,which load the hardware model on fabrication machines that manufacturethe integrated circuit. The integrated circuit may be fabricated suchthat the circuit performs operations described in association with anyof the embodiments described herein.

FIG. 11A is a block diagram illustrating an IP core development system1100 that may be used to manufacture an integrated circuit to performoperations according to an embodiment. The IP core development system1100 may be used to generate modular, re-usable designs that can beincorporated into a larger design or used to construct an entireintegrated circuit (e.g., an SOC integrated circuit). A design facility1130 can generate a software simulation 1110 of an IP core design in ahigh-level programming language (e.g., C/C++). The software simulation1110 can be used to design, test, and verify the behavior of the IP coreusing a simulation model 1112. The simulation model 1112 may includefunctional, behavioral, and/or timing simulations. A register transferlevel (RTL) design 1115 can then be created or synthesized from thesimulation model 1112. The RTL design 1115 is an abstraction of thebehavior of the integrated circuit that models the flow of digitalsignals between hardware registers, including the associated logicperformed using the modeled digital signals. In addition to an RTLdesign 1115, lower-level designs at the logic level or transistor levelmay also be created, designed, or synthesized. Thus, the particulardetails of the initial design and simulation may vary.

The RTL design 1115 or equivalent may be further synthesized by thedesign facility into a hardware model 1120, which may be in a hardwaredescription language (HDL), or some other representation of physicaldesign data. The HDL may be further simulated or tested to verify the IPcore design. The IP core design can be stored for delivery to a 3^(rd)party fabrication facility 1165 using non-volatile memory 1140 (e.g.,hard disk, flash memory, or any non-volatile storage medium).Alternatively, the IP core design may be transmitted (e.g., via theInternet) over a wired connection 1150 or wireless connection 1160. Thefabrication facility 1165 may then fabricate an integrated circuit thatis based at least in part on the IP core design. The fabricatedintegrated circuit can be configured to perform operations in accordancewith at least one embodiment described herein.

FIG. 11B illustrates a cross-section side view of an integrated circuitpackage assembly 1170, according to some embodiments described herein.The integrated circuit package assembly 1170 illustrates animplementation of one or more processor or accelerator devices asdescribed herein. The package assembly 1170 includes multiple units ofhardware logic 1172, 1174 connected to a substrate 1180. The logic 1172,1174 may be implemented at least partly in configurable logic orfixed-functionality logic hardware, and can include one or more portionsof any of the processor core(s), graphics processor(s), or otheraccelerator devices described herein. Each unit of logic 1172, 1174 canbe implemented within a semiconductor die and coupled with the substrate1180 via an interconnect structure 1173. The interconnect structure 1173may be configured to route electrical signals between the logic 1172,1174 and the substrate 1180, and can include interconnects such as, butnot limited to bumps or pillars. In some embodiments, the interconnectstructure 1173 may be configured to route electrical signals such as,for example, input/output (I/O) signals and/or power or ground signalsassociated with the operation of the logic 1172, 1174. In someembodiments, the substrate 1180 is an epoxy-based laminate substrate.The package substrate 1180 may include other suitable types ofsubstrates in other embodiments. The package assembly 1170 can beconnected to other electrical devices via a package interconnect 1183.The package interconnect 1183 may be coupled to a surface of thesubstrate 1180 to route electrical signals to other electrical devices,such as a motherboard, other chipset, or multi-chip module.

In some embodiments, the units of logic 1172, 1174 are electricallycoupled with a bridge 1182 that is configured to route electricalsignals between the logic 1172, 1174. The bridge 1182 may be a denseinterconnect structure that provides a route for electrical signals. Thebridge 1182 may include a bridge substrate composed of glass or asuitable semiconductor material. Electrical routing features can beformed on the bridge substrate to provide a chip-to-chip connectionbetween the logic 1172, 1174.

Although two units of logic 1172, 1174 and a bridge 1182 areillustrated, embodiments described herein may include more or fewerlogic units on one or more dies. The one or more dies may be connectedby zero or more bridges, as the bridge 1182 may be excluded when thelogic is included on a single die. Alternatively, multiple dies or unitsof logic can be connected by one or more bridges. Additionally, multiplelogic units, dies, and bridges can be connected together in otherpossible configurations, including three-dimensional configurations.

Exemplary System on a Chip Integrated Circuit

FIGS. 12-14 illustrated exemplary integrated circuits and associatedgraphics processors that may be fabricated using one or more IP cores,according to various embodiments described herein. In addition to whatis illustrated, other logic and circuits may be included, includingadditional graphics processors/cores, peripheral interface controllers,or general-purpose processor cores.

FIG. 12 is a block diagram illustrating an exemplary system on a chipintegrated circuit 1200 that may be fabricated using one or more IPcores, according to an embodiment. Exemplary integrated circuit 1200includes one or more application processor(s) 1205 (e.g., CPUs), atleast one graphics processor 1210, and may additionally include an imageprocessor 1215 and/or a video processor 1220, any of which may be amodular IP core from the same or multiple different design facilities.Integrated circuit 1200 includes peripheral or bus logic including a USBcontroller 1225, UART controller 1230, an SPI/SDIO controller 1235, andan I²S/I²C controller 1240. Additionally, the integrated circuit caninclude a display device 1245 coupled to one or more of ahigh-definition multimedia interface (HDMI) controller 1250 and a mobileindustry processor interface (MIPI) display interface 1255. Storage maybe provided by a flash memory subsystem 1260 including flash memory anda flash memory controller. Memory interface may be provided via a memorycontroller 1265 for access to SDRAM or SRAM memory devices. Someintegrated circuits additionally include an embedded security engine1270.

FIGS. 13A-13B are block diagrams illustrating exemplary graphicsprocessors for use within an SoC, according to embodiments describedherein. FIG. 13A illustrates an exemplary graphics processor 1310 of asystem on a chip integrated circuit that may be fabricated using one ormore IP cores, according to an embodiment. FIG. 13B illustrates anadditional exemplary graphics processor 1340 of a system on a chipintegrated circuit that may be fabricated using one or more IP cores,according to an embodiment. Graphics processor 1310 of FIG. 13A is anexample of a low power graphics processor core. Graphics processor 1340of FIG. 13B is an example of a higher performance graphics processorcore. Each of the graphics processors 1310, 1340 can be variants of thegraphics processor 1210 of FIG. 12.

As shown in FIG. 13A, graphics processor 1310 includes a vertexprocessor 1305 and one or more fragment processor(s) 1315A-1315N (e.g.,1315A, 1315B, 1315C, 1315D, through 1315N-1, and 1315N). Graphicsprocessor 1310 can execute different shader programs via separate logic,such that the vertex processor 1305 is optimized to execute operationsfor vertex shader programs, while the one or more fragment processor(s)1315A-1315N execute fragment (e.g., pixel) shading operations forfragment or pixel shader programs. The vertex processor 1305 performsthe vertex processing stage of the 3D graphics pipeline and generatesprimitives and vertex data. The fragment processor(s) 1315A-1315N usethe primitive and vertex data generated by the vertex processor 1305 toproduce a framebuffer that is displayed on a display device. In oneembodiment, the fragment processor(s) 1315A-1315N are optimized toexecute fragment shader programs as provided for in the OpenGL API,which may be used to perform similar operations as a pixel shaderprogram as provided for in the Direct 3D API.

Graphics processor 1310 additionally includes one or more memorymanagement units (MMUs) 1320A-1320B, cache(s) 1325A-1325B, and circuitinterconnect(s) 1330A-1330B. The one or more MMU(s) 1320A-1320B providefor virtual to physical address mapping for the graphics processor 1310,including for the vertex processor 1305 and/or fragment processor(s)1315A-1315N, which may reference vertex or image/texture data stored inmemory, in addition to vertex or image/texture data stored in the one ormore cache(s) 1325A-1325B. In one embodiment, the one or more MMU(s)1320A-1320B may be synchronized with other MMUs within the system,including one or more MMUs associated with the one or more applicationprocessor(s) 1205, image processor 1215, and/or video processor 1220 ofFIG. 12, such that each processor 1205-1220 can participate in a sharedor unified virtual memory system. The one or more circuitinterconnect(s) 1330A-1330B enable graphics processor 1310 to interfacewith other IP cores within the SoC, either via an internal bus of theSoC or via a direct connection, according to embodiments.

As shown FIG. 13B, graphics processor 1340 includes the one or moreMMU(s) 1320A-1320B, caches 1325A-1325B, and circuit interconnects1330A-1330B of the graphics processor 1310 of FIG. 13A. Graphicsprocessor 1340 includes one or more shader core(s) 1355A-1355N (e.g.,1455A, 1355B, 1355C, 1355D, 1355E, 1355F, through 1355N-1, and 1355N),which provides for a unified shader core architecture in which a singlecore or type or core can execute all types of programmable shader code,including shader program code to implement vertex shaders, fragmentshaders, and/or compute shaders. The exact number of shader corespresent can vary among embodiments and implementations. Additionally,graphics processor 1340 includes an inter-core task manager 1345, whichacts as a thread dispatcher to dispatch execution threads to one or moreshader cores 1355A-1355N and a tiling unit 1358 to accelerate tilingoperations for tile-based rendering, in which rendering operations for ascene are subdivided in image space, for example to exploit localspatial coherence within a scene or to optimize use of internal caches.

FIGS. 14A-14B illustrate additional exemplary graphics processor logicaccording to embodiments described herein. FIG. 14A illustrates agraphics core 1400 that may be included within the graphics processor1210 of FIG. 12, and may be a unified shader core 1355A-1355N as in FIG.13B. FIG. 14B illustrates an additional general-purpose graphicsprocessing unit 1430, which is a highly-parallel general-purposegraphics processing unit suitable for deployment on a multi-chip module.

As shown in FIG. 14A, the graphics core 1400 includes a sharedinstruction cache 1402, a texture unit 1418, and a cache/shared memory1420 that are common to the execution resources within the graphics core1400. The graphics core 1400 can include multiple slices 1401A-1401N orpartition for each core, and a graphics processor can include multipleinstances of the graphics core 1400. The slices 1401A-1401N can includesupport logic including a local instruction cache 1404A-1404N, a threadscheduler 1406A-1406N, a thread dispatcher 1408A-1408N, and a set ofregisters 1410A-1440N. To perform logic operations, the slices1401A-1401N can include a set of additional function units (AFUs1412A-1412N), floating-point units (FPU 1414A-1414N), integer arithmeticlogic units (ALUs 1416-1416N), address computational units (ACU1413A-1413N), double-precision floating-point units (DPFPU 1415A-1415N),and matrix processing units (MPU 1417A-1417N).

Some of the computational units operate at a specific precision. Forexample, the FPUs 1414A-1414N can perform single-precision (32-bit) andhalf-precision (16-bit) floating point operations, while the DPFPUs1415A-1415N perform double precision (64-bit) floating point operations.The ALUs 1416A-1416N can perform variable precision integer operationsat 8-bit, 16-bit, and 32-bit precision, and can be configured for mixedprecision operations. The MPUs 1417A-1417N can also be configured formixed precision matrix operations, including half-precision floatingpoint and 8-bit integer operations. The MPUs 1417-1417N can perform avariety of matrix operations to accelerate machine learning applicationframeworks, including enabling support for accelerated general matrix tomatrix multiplication (GEMM). The AFUs 1412A-1412N can performadditional logic operations not supported by the floating-point orinteger units, including trigonometric operations (e.g., Sine, Cosine,etc.).

As shown in FIG. 14B, a general-purpose processing unit (GPGPU) 1430 canbe configured to enable highly-parallel compute operations to beperformed by an array of graphics processing units. Additionally, theGPGPU 1430 can be linked directly to other instances of the GPGPU tocreate a multi-GPU cluster to improve training speed for particularlydeep neural networks. The GPGPU 1430 includes a host interface 1432 toenable a connection with a host processor. In one embodiment, the hostinterface 1432 is a PCI Express interface. However, the host interfacecan also be a vendor specific communications interface or communicationsfabric. The GPGPU 1430 receives commands from the host processor anduses a global scheduler 1434 to distribute execution threads associatedwith those commands to a set of compute clusters 1436A-1436H. Thecompute clusters 1436A-1436H share a cache memory 1438. The cache memory1438 can serve as a higher-level cache for cache memories within thecompute clusters 1436A-1436H.

The GPGPU 1430 includes memory 1444A-1444B coupled with the computeclusters 1436A-1436H via a set of memory controllers 1442A-1442B. Invarious embodiments, the memory 1444A-1444B can include various types ofmemory devices including dynamic random-access memory (DRAM) or graphicsrandom access memory, such as synchronous graphics random access memory(SGRAM), including graphics double data rate (GDDR) memory.

In one embodiment, the compute clusters 1436A-1436H each include a setof graphics cores, such as the graphics core 1400 of FIG. 14A, which caninclude multiple types of integer and floating point logic units thatcan perform computational operations at a range of precisions includingsuited for machine learning computations. For example, and in oneembodiment at least a subset of the floating point units in each of thecompute clusters 1436A-1436H can be configured to perform 16-bit or32-bit floating point operations, while a different subset of thefloating point units can be configured to perform 64-bit floating pointoperations.

Multiple instances of the GPGPU 1430 can be configured to operate as acompute cluster. The communication mechanism used by the compute clusterfor synchronization and data exchange varies across embodiments. In oneembodiment, the multiple instances of the GPGPU 1430 communicate overthe host interface 1432. In one embodiment, the GPGPU 1430 includes anI/O hub 1439 that couples the GPGPU 1430 with a GPU link 1440 thatenables a direct connection to other instances of the GPGPU. In oneembodiment, the GPU link 1440 is coupled to a dedicated GPU-to-GPUbridge that enables communication and synchronization between multipleinstances of the GPGPU 1430. In one embodiment, the GPU link 1440couples with a high-speed interconnect to transmit and receive data toother GPGPUs or parallel processors. In one embodiment, the multipleinstances of the GPGPU 1430 are located in separate data processingsystems and communicate via a network device that is accessible via thehost interface 1432. In one embodiment, the GPU link 1440 can beconfigured to enable a connection to a host processor in addition to oras an alternative to the host interface 1432.

While the illustrated configuration of the GPGPU 1430 can be configuredto train neural networks, one embodiment provides alternateconfiguration of the GPGPU 1430 that can be configured for deploymentwithin a high performance or low power inferencing platform. In aninferencing configuration, the GPGPU 1430 includes fewer of the computeclusters 1436A-1436H relative to the training configuration.Additionally, the memory technology associated with the memory1434A-1434B may differ between inferencing and training configurations,with higher bandwidth memory technologies devoted to trainingconfigurations. In one embodiment, the inferencing configuration of theGPGPU 1430 can support inferencing specific instructions. For example,an inferencing configuration can provide support for one or more 8-bitinteger dot product instructions, which are commonly used duringinferencing operations for deployed neural networks.

FIG. 15 illustrates a computing device 1500 hosting a point-cloud scenereconstruction mechanism (“reconstruction mechanism”) 1510 according toone embodiment. Computing device 2000 represents a communication anddata processing device including (but not limited to) smart wearabledevices, smartphones, virtual reality (VR) devices, head-mounted display(HMDs), mobile computers, Internet of Things (IoT) devices, laptopcomputers, desktop computers, server computers, etc., and be similar toor the same as processing device 100 of FIG. 1; accordingly, forbrevity, clarity, and ease of understanding, many of the details statedabove with reference to FIGS. 1-14B are not further discussed orrepeated hereafter.

Computing device 1500 may further include (without limitations) anautonomous machine or an artificially intelligent agent, such as amechanical agent or machine, an electronics agent or machine, a virtualagent or machine, an electro-mechanical agent or machine, etc. Examplesof autonomous machines or artificially intelligent agents may include(without limitation) robots, autonomous vehicles (e.g., self-drivingcars, self-flying planes, self-sailing boats, etc.), autonomousequipment (self-operating construction vehicles, self-operating medicalequipment, etc.), and/or the like. Throughout this document, “computingdevice” may be interchangeably referred to as “autonomous machine” or“artificially intelligent agent” or simply “robot”.

It contemplated that although “autonomous vehicle” and “autonomousdriving” are referenced throughout this document, embodiments are notlimited as such. For example, “autonomous vehicle” is not limed to anautomobile but that it may include any number and type of autonomousmachines, such as robots, autonomous equipment, household autonomousdevices, and/or the like, and any one or more tasks or operationsrelating to such autonomous machines may be interchangeably referencedwith autonomous driving.

Computing device 1500 may further include (without limitations) largecomputing systems, such as server computers, desktop computers, etc.,and may further include set-top boxes (e.g., Internet-based cabletelevision set-top boxes, etc.), global positioning system (GPS)-baseddevices, etc. Computing device 1500 may include mobile computing devicesserving as communication devices, such as cellular phones includingsmartphones, personal digital assistants (PDAs), tablet computers,laptop computers, e-readers, smart televisions, television platforms,wearable devices (e.g., glasses, watches, bracelets, smartcards,jewelry, clothing items, etc.), media players, etc. For example, in oneembodiment, computing device 600 may include a mobile computing deviceemploying a computer platform hosting an integrated circuit (“IC”), suchas system on a chip (“SoC” or “SOC”), integrating various hardwareand/or software components of computing device 1500 on a single chip.

As illustrated, in one embodiment, computing device 1500 may include anynumber and type of hardware and/or software components, such as (withoutlimitation) graphics processing unit (“GPU” or simply “graphicsprocessor”) 1514, graphics driver (also referred to as “GPU driver”,“graphics driver logic”, “driver logic”, user-mode driver (UMD), UMD,user-mode driver framework (UMDF), UMDF, or simply “driver”) 1516,central processing unit (“CPU” or simply “application processor”) 1512,memory 1508, network devices, drivers, or the like, as well asinput/output (I/O) sources 1504, such as touchscreens, touch panels,touch pads, virtual or regular keyboards, virtual or regular mice,ports, connectors, etc. Computing device 1500 may include operatingsystem (OS) 1506 serving as an interface between hardware and/orphysical resources of the computer device 1500 and a user. It iscontemplated that graphics processor 2014 and application processor 2012may be one or more of processor(s) 102 of FIG. 1.

It is to be appreciated that a lesser or more equipped system than theexample described above may be preferred for certain implementations.Therefore, the configuration of computing device 1500 may vary fromimplementation to implementation depending upon numerous factors, suchas price constraints, performance requirements, technologicalimprovements, or other circumstances.

Embodiments may be implemented as any or a combination of: one or moremicrochips or integrated circuits interconnected using a parentboard,hardwired logic, software stored by a memory device and executed by amicroprocessor, firmware, an application specific integrated circuit(ASIC), and/or a field programmable gate array (FPGA). The terms“logic”, “module”, “component”, “engine”, “mechanism”, “tool”,“circuit”, and “circuitry” are referenced interchangeably throughoutthis document and include, by way of example, software, hardware,firmware, or any combination thereof.

In one embodiment, as illustrated, reconstruction mechanism 1510 may behosted by memory 1508 of computing device 1500. In another embodiment,reconstruction mechanism 1510 may be hosted by operating system 1510 orgraphics driver 1516. In yet another embodiment, reconstructionmechanism 1510 may be hosted by or part of graphics processing unit(“GPU” or simply graphics processor”) 1514 or firmware of graphicsprocessor 1514. For example, reconstruction mechanism 1510 may beembedded in or implemented as part of the processing hardware ofgraphics processor 1514. Similarly, in yet another embodiment,reconstruction mechanism 1510 may be hosted by or part of centralprocessing unit (“CPU” or simply “application processor”) 1512. Forexample, reconstruction mechanism 1510 may be embedded in or implementedas part of the processing hardware of application processor 1512.

In yet another embodiment, reconstruction mechanism 1510 may be hostedby or part of any number and type of components of computing device1500, such as a portion of reconstruction mechanism 1510 may be hostedby or part of operating system 1506, another portion may be hosted by orpart of graphics processor 1514, another portion may be hosted by orpart of application processor 1512, while one or more portions ofreconstruction mechanism 1510 may be hosted by or part of operatingsystem 1506 and/or any number and type of devices of computing device1500. It is contemplated that embodiments are not limited to anyimplementation or hosting of reconstruction mechanism 1510 and that oneor more portions or components of reconstruction mechanism 1510 may beemployed or implemented as hardware, software, or any combinationthereof, such as firmware.

Computing device 1500 may host network interface(s) to provide access toa network, such as a LAN, a wide area network (WAN), a metropolitan areanetwork (MAN), a personal area network (PAN), Bluetooth, a cloudnetwork, a mobile network (e.g., 3^(rd) Generation (3G), 4^(th)Generation (4G), etc.), an intranet, the Internet, etc. Networkinterface(s) may include, for example, a wireless network interfacehaving antenna, which may represent one or more antenna(e). Networkinterface(s) may also include, for example, a wired network interface tocommunicate with remote devices via network cable, which may be, forexample, an Ethernet cable, a coaxial cable, a fiber optic cable, aserial cable, or a parallel cable.

Embodiments may be provided, for example, as a computer program productwhich may include one or more machine-readable media having storedthereon machine-executable instructions that, when executed by one ormore machines such as a computer, network of computers, or otherelectronic devices, may result in the one or more machines carrying outoperations in accordance with embodiments described herein. Amachine-readable medium may include, but is not limited to, floppydiskettes, optical disks, CD-ROMs (Compact Disc-Read Only Memories), andmagneto-optical disks, ROMs, RAMs, EPROMs (Erasable Programmable ReadOnly Memories), EEPROMs (Electrically Erasable Programmable Read OnlyMemories), magnetic or optical cards, flash memory, or other type ofmedia/machine-readable medium suitable for storing machine-executableinstructions.

Moreover, embodiments may be downloaded as a computer program product,wherein the program may be transferred from a remote computer (e.g., aserver) to a requesting computer (e.g., a client) by way of one or moredata signals embodied in and/or modulated by a carrier wave or otherpropagation medium via a communication link (e.g., a modem and/ornetwork connection).

Throughout the document, term “user” may be interchangeably referred toas “viewer”, “observer”, “person”, “individual”, “end-user”, and/or thelike. It is to be noted that throughout this document, terms like“graphics domain” may be referenced interchangeably with “graphicsprocessing unit”, “graphics processor”, or simply “GPU” and similarly,“CPU domain” or “host domain” may be referenced interchangeably with“computer processing unit”, “application processor”, or simply “CPU”.

It is to be noted that terms like “node”, “computing node”, “server”,“server device”, “cloud computer”, “cloud server”, “cloud servercomputer”, “machine”, “host machine”, “device”, “computing device”,“computer”, “computing system”, and the like, may be usedinterchangeably throughout this document. It is to be further noted thatterms like “application”, “software application”, “program”, “softwareprogram”, “package”, “software package”, and the like, may be usedinterchangeably throughout this document. Also, terms like “job”,“input”, “request”, “message”, and the like, may be used interchangeablythroughout this document.

FIG. 16 illustrates point-cloud reconstruction mechanism 1510 of FIG. 15according to one embodiment. For brevity, many of the details alreadydiscussed with reference to FIGS. 1-15 are not repeated or discussedhereafter. In one embodiment, reconstruction mechanism 1510 may includeany number and type of components, such as (without limitations):detection and selection logic 1601; coarse visual hulling logic (“coarselogic”) 1603; fine visual hulling logic (“fine logic”) 1605;communication/compatibility logic 1607; distance field and normalcomputation logic (“computation logic”) 1609; visibility logic 1611;photo-consistency logic 1613; and refinement and application logic 1615.

As previously described, embodiments provide for a novel technique,through reconstruction mechanism 1510, to produce point clouds, asopposed to voxelized level set surfaces, since point clouds storeinformation for just the surface of each reconstructed object (which isdifferent from and better than the voxelized level sets requiringvolumetric storage). Moreover, point clouds are an explicit (lagrangian)representation that can embed information about color variation thatoccurs as a result of viewing direction. In contrast, voxelized levelsets are an implicit (eulerian) representation that cannot easily embedcolor/lighting variation.

In one embodiment, reconstruction mechanism 1510 provides for performinga stochastic visual hull reconstruction to locate objects of interest ina scene, where bounding boxes are constructed around each object ofinterest, while a dense hull is reconstructed using a cloudrepresentation. For example, points well inside the object are culled,while points near the surface are moved along surface normal to maximizephoto-consistency. The point cloud is then smoothed usingnearest-neighbors to estimate local surface curvature. Further, colorand visibility information are associated with each point forview-dependent rendering.

This point cloud representation, as facilitated by reconstructionmechanism 1510, is used throughout the pipeline enabling greaterparallelization of the reconstruction process on a graphics card with arelatively smaller memory footprint than conventional techniques likemulti-view stereo matching, depth map estimation, graph cuts, and levelset evolution, etc.

Embodiments provide for reconstruction, fine camera calibration,multi-view ground-plane projection segmentation, and motion sequenceground-plane projection segmentation, etc., in addition to offering coderepository contents. In one embodiment, detection and selection logic1601 is used for detection of scenes and their objects and empty spaces.For example, detection and selection logic 1601 detects a scene and itsobjects and empty spaces and subsequently, selects much of the emptyspaces for culling.

In one embodiment, reconstruction proceed with culling of as much of theempty space as feasible using coarse visual hulling as facilitated bycoarse logic 1603. Further, fine visual hulling of the non-empty regionsof the scene is then performed using fine logic 1605. At this point, itbecomes known that objects and their shapes be contained within thehulls. In one embodiment, photo-consistency (so that any points visiblein multiple cameras may appear similar) may be applied to refine theshapes of objects as facilitated by photo-consistency logic 1613,followed by smoothing of results and outputting of a point cloud asfacilitated by refinement and application logic 1615.

For example, this novel reconstruction technique may involve thefollowing processes, as parallelizable as feasible: 1) computing acoarse-to-fine visual hull computation to find regions in a volume wherethere are shapes of interest (such as players, referees, balls, playingarea, equipment, etc., in case of a scene representing a sportingevent); 2) computing a fine-scale visual hull computation in a set ofvoxelized regions containing shapes of interest to determine voxel byvoxel occupancy of the visual hull; 3) using a distance field around anyoccupied voxels to compute normal; 4) using the distance field, creatinga denser point cloud, and computing visibility of each point to eachcamera; 5) moving each point along its normal and trying to improvephoto-consistency and then smoothing the result by moving each pointtowards a plane defined by it and its neighbors; and 6) outputting apoint cloud, where for each point, there is a normal, a list of cameraidentifications that can see the point, and the coordinates into acamera image.

Further, in one embodiment, this novel technique and its algorithm maybe created and performed with shaders, while, in another embodiment,this novel technique and its algorithm may be written in OpenCL®, CUDA®,and/or the like, using kernels. It is contemplated, however, thatembodiments are not limited to any implementations, techniques,protocols, programming languages, development platforms and/or the like.

In one embodiment, an octree spanning the entire volume of interest iscreated and fully expanded at an initial grid size. For example, in caseof a sporting event like football, the initial octree cubes may be 0.25m per side, so the initial grid may measure to be 400×200×12. For eachoctree cube, a determination is made as to whether it has a foregroundelement by, for example, considering random points (e.g., 8 randompoints) within an octree cell. For each of these randomly placed points,another determination is made as to whether it is considered to beforeground in segmentations and if yes, then the octree node is selectedand marked to be empty as facilitated by detection and selection logic1601, while the empty space is then culled using coarse logic 1603.

Upon culling of the empty space by coarse logic 1603, any non-emptyoctree cells are then subdivided, and the same process is repeated forany newly-created cells. For example, this process may be repeated forseveral levels, such as three levels, of subdivision until the octreecells are reduced to a fraction of their original dimensions, such as ⅛of the original dimensions, 3 cm of their original size, etc. Further,for example, when testing a point for visibility in a segmentation,floating points arithmetic may be used to project the scene onto theimage plane, followed by bilinear interpolation of the segmented image.If zero represents a background and one represents a foreground, then acertain threshold, such as threshold 0.5, is used to establish whetherthe point is foreground or background.

Using the process described above as facilitated by coarse logic 1603,information is obtained to indicate where the objects of interests arelocated within the scene. For example, in a CPU-implementation, octreesare continuously refined; however, to obtain better performance, GPUparallelism may be used, such as when the computation is fullyvectorizable. At this point, a switch is made from using octrees tousing sets of dense voxel grids, such as 128×128×128 dense voxel grids,to cover all the points of interest from the octree, while ensuring theyoverlap by a guard band. The guard band further ensures that each densevoxel grid is independent of other, such as by taking into account allthe ways that the output at one point can depend on the neighbors asfacilitated by fine logic 1605.

For example, in case of a scene from a football game, dense voxel gridsmay be 5 mm per side, while on GPU 1514, each voxel center is projectedon to the segmented images for the images whose cameras include voxelcenters, where foreground and background are determined as before byusing bilinear interpolation and thresholding as facilitated by finelogic 1605.

In some cases, any voxel that is determined to be in the background is asingle image is removed, but this is modified to compute a percentage ofsegmented images in which the voxels appear to be in the background andpotentially give more opportunities to be robust in the face ofincorrect segmentation.

Further, in one embodiment, computation logic 1609 may be used tocompute a distance field around the voxels that remain. This isperformed by a small number of updates across an entire voxel grid thatpropagate the distance to the immediate neighbors, such as starting atzero for the voxels in the fine visual hull. This process is continueduntil the distance field is valid out, such as 1 cm, from the originalpoints. Then, computation logic 1609 is used to compute the gradient ofthe distance field at each voxel in the fine visual hull and use that asthe normal. The normal can have a variety of uses, but only some ofwhich may be explored, such as they may be used for photo-consistencycomputations along with being useful for rendering purposes.

For photo-consistency and rendering, in one embodiment, it is knownwhich points in the point cloud are visible from which cameras such thatexcept for the points on the surface, all other points are removed asfacilitated by visibility logic 1611. For example, this process isachieved by first making sure that the point cloud is dense enough toavoid any holes and then, a Z-buffer rendering is performed for each ofthe cameras to identify the front surfaces as facilitated by visibilitylogic 1611.

Further, around each cell in the fine visual hull is a band of cells,about 1 cm thick, where the distance field is computed by computationlogic 1609. At a random point in each of the cells, a new potentialsurface point is created and then, each of the potential surface pointsis removed to the zero of the interpolated distance field by followingthe gradient as facilitated by visibility logic 1611, which results inan oversampled set of points near the surface. Then, in one embodiment,visibility logic 1611 is used to render the oversampled set of pointsusing, for example, an OpenGL® Z buffer, and find the ones that arevisible in each camera image. Those points that are not visible in anycamera images are discarded because it is not known how they look likeand may just be the interior points. Now, with a dense set of points onthe surface of the visual hull, for each point, a normal and a list ofcameras is obtained and visible.

With perfect segmentation, the shapes are contained within a boundingvisual hull, but the bounding visual hull may not reveal concavities. Tofind such concavities and compensate for inaccuracies in thesegmentation, the approximate result is adjusted from the visual hullcomputation to improve photo-consistency as facilitated byphoto-consistency logic 1613. For example, each point along the surfacenormal may be moved and a best displacement is found to improve thephoto-consistency score.

In one embodiment, photo-consistency logic 1613 is used to computephoto-consistency as follows: consider a point P and test a series ofdisplacement vectors V_(i) such that each displacement vector offers adisplaced point P+V_(i). With the displaced point as the center, a 3×3set of points S_(i) is created in the tangent plane, where S_(i)contains 9 points. This set of 9 points is then used and projected intoeach of the cameras in which the point P is visible. For example, ifthere are k cameras where P is visible, k vectors of image colors R_(ik)is obtained. These k vectors of image colors are then averaged to obtainan average set of colors, M _(i). Further, the average L1 norm of thedifference between R_(ik) and M _(i) is computed over the k views, wherethis norm is a photo-consistency error, and that displacement V_(i) withthe minimum photo-consistency error is chosen.

In some cases, there is a clear minimum to the photo-consistency error,in which case, a position of the displaced point is regarded asreliable. In some other cases, the photo-consistency error may have asmall gradient as one moves along the normal vector such that where thephoto-consistency gradient is small, a normal displacement relies on theneighbors, the smoothness of the surface may be a tradeoff for aphoto-consistency error.

In one embodiment, photo-consistency logic 1613 may be used to addressthis issue by doing smoothing after the photo-consistency displacementsince the displacement is computed independently for each point, wherethe result may become unsmooth. This is addressed and resolved by aseries of iterations that bring each point towards a best-fit planeformed by it and its neighbors as facilitated by photo-consistency logic1613.

For each point, neighbors within a small fixed distance are found andthen a principal component analysis is performed for such points usingrefinement and application logic 1615. This tangent plane is estimatedas the plan through the centroid of the neighborhood which isperpendicular to the principal component corresponding to the largesteigenvector as facilitated by refinement and application logic 1615. Forexample, a point is moved back towards the tangent plane by a fractiondependent on the photo-consistency error such that smallphoto-consistency errors are moved the least, while the large ones aremoved the most.

Further, simply using a plane perpendicular to the distance-fieldgradient to estimate the tangent plane works as the principal componentanalysis with less overall computation. At some points, thedistance-field gradient vanishes and at those points, the principalcomponent analysis is relied upon. At points where it is difficult toestimate the tangent plane, each point is simply moved towards thecentroid of the neighbors and after a series of smoothing iterations,the point cloud computation is completed as facilitated by refinementand application logic 1615. Then, using refinement and application logic1615, the points are outputted along with their normal and a list ofcameras identifications (IDs) in which each point is visible.

Computing device 1500 is further shown to be in communication with oneor more repositories, datasets, and/or databases, such as database(s)1630 (e.g., cloud storage, non-cloud storage, etc.), where database(s)1630 may reside at a local storage or a remote storage overcommunication medium(s) 1625, such as one or more networks (e.g., cloudnetwork, proximity network, mobile network, intranet, Internet, etc.).

It is contemplated that a software application running at computingdevice 1500 may be responsible for performing or facilitatingperformance of any number and type of tasks using one or more components(e.g., GPU 1514, graphics driver 1516, CPU 1512, etc.) of computingdevice 1500. When performing such tasks, as defined by the softwareapplication, one or more components, such as GPU 1514, graphics driver1516, CPU 1512, etc., may communicate with each other to ensure accurateand timely processing and completion of those tasks.

Communication/compatibility logic 1607 may be used to facilitate theneeded communication and compatibility between any number of devices ofcomputing device 1500 and various components of reconstruction mechanism1510.

Communication/compatibility logic 1607 may be used to facilitate dynamiccommunication and compatibility between computing device 1500 and anynumber and type of other computing devices (such as mobile computingdevice, desktop computer, server computing device, etc.); processingdevices or components (such as CPUs, GPUs, etc.);capturing/sensing/detecting devices (such as capturing/sensingcomponents including cameras, depth sensing cameras, camera sensors, redgreen blue (RGB) sensors, microphones, etc.); display devices (such asoutput components including display screens, display areas, displayprojectors, etc.); user/context-awareness components and/oridentification/verification sensors/devices (such as biometricsensors/detectors, scanners, etc.); database(s) 1630, such as memory orstorage devices, databases, and/or data sources (such as data storagedevices, hard drives, solid-state drives, hard disks, memory cards ordevices, memory circuits, etc.); communication medium(s) 1625, such asone or more communication channels or networks (e.g., Cloud network, theInternet, intranet, cellular network, proximity networks, such asBluetooth, Bluetooth low energy (BLE), Bluetooth Smart, Wi-Fi proximity,Radio Frequency Identification (RFID), Near Field Communication (NFC),Body Area Network (BAN), etc.); wireless or wired communications andrelevant protocols (e.g., Wi-Fi®, WiMAX, Ethernet, etc.); connectivityand location management techniques; software applications/websites(e.g., social and/or business networking websites, etc., businessapplications, games and other entertainment applications, etc.); andprogramming languages, etc., while ensuring compatibility with changingtechnologies, parameters, protocols, standards, etc.

Throughout this document, terms like “logic”, “component”, “module”,“framework”, “engine”, “mechanism”, “circuit”, and “circuitry”, and/orthe like, are referenced interchangeably and may include, by way ofexample, software, hardware, firmware, or any combination thereof. Inone example, “logic” may refer to or include a software component thatis capable of working with one or more of an operating system (e.g.,operating system 1506), a graphics driver (e.g., graphics driver 1516),etc., of a computing device, such as computing device 1500. In anotherexample, “logic” may refer to or include a hardware component that iscapable of being physically installed along with or as part of one ormore system hardware elements, such as an application processor (e.g.,CPU 2012), a graphics processor (e.g., GPU 1514), etc., of a computingdevice, such as computing device 1500. In yet another embodiment,“logic” may refer to or include a firmware component that is capable ofbeing part of system firmware, such as firmware of an applicationprocessor (e.g., CPU 1512) or a graphics processor (e.g., GPU 1514),etc., of a computing device, such as computing device 1500.

Further, any use of a particular brand, word, term, phrase, name, and/oracronym, such as “reconstruction”, “point cloud reconstruction”, “coarsevisual hull”, “fine visual hull”, “distance field and normal”,“visibility”, “photo-consistency”, “smoothness”, “output”, “GPU”, “GPUdomain”, “GPGPU”, “CPU”, “CPU domain”, “graphics driver”, “workload”,“application”, “graphics pipeline”, “pipeline processes”, “register”,“register file”, “RF”, “extended register file”, “ERF”, “executionunit”, “EU”, “instruction”, “API”, “3D API”, “OpenGL®”, “DirectX®”,“fragment shader”, “YUV texture”, “shader execution”, “existing UAVcapabilities”, “existing backend”, “hardware”, “software”, “agent”,“graphics driver”, “kernel mode graphics driver”, “user-mode driver”,“user-mode driver framework”, “buffer”, “graphics buffer”, “task”,“process”, “operation”, “software application”, “game”, etc., should notbe read to limit embodiments to software or devices that carry thatlabel in products or in literature external to this document.

It is contemplated that any number and type of components may be addedto and/or removed from reconstruction mechanism 1510 to facilitatevarious embodiments including adding, removing, and/or enhancing certainfeatures. For brevity, clarity, and ease of understanding ofreconstruction mechanism 1510, many of the standard and/or knowncomponents, such as those of a computing device, are not shown ordiscussed here. It is contemplated that embodiments, as describedherein, are not limited to any technology, topology, system,architecture, and/or standard and are dynamic enough to adopt and adaptto any future changes.

FIG. 17A-17K illustrate a transaction sequence for facilitating pointcloud reconstruction of scenes according to one embodiment. For brevity,many of the details previously discussed with reference to FIGS. 1-16may not be discussed or repeated hereafter. Any processes relating totransaction sequence may be performed by processing logic that maycomprise hardware (e.g., circuitry, dedicated logic, programmable logic,etc.), software (such as instructions run on a processing device), or acombination thereof, as facilitated by reconstruction mechanism 1510 ofFIG. 15. The processes associated with transaction sequence may beillustrated or recited in linear sequences for brevity and clarity inpresentation; however, it is contemplated that any number of them can beperformed in parallel, asynchronously, or in different orders.

At illustrated in FIG. 17A, search grid 1700 of a scene is detected andselected as an initial search grid by detection and selection logic 1601of FIG. 16 for visual hull clustering as facilitated by coarse logic1603 of FIG. 16. For example, this illustrated top-down view of thefield within search grid 1700 includes objects of interest 1703A, 1703Bcaptured by cameras 1701 to be reconstructed. This reconstruction isinitiated by performing coarse visual hulling using coarse logic 1603 onobjects 1703A, 1703B within visual hulls 1705A, 1705B as determined fromthe overlapping ranges of cameras 1701.

In some embodiments, depending on the available memory and other systemresources, multiple objects 1703A, 1703B may be reconstructedsimultaneously (such as in case of sufficient memory and/or otherresources) or singularly (such as in case of not having sufficientmemory and/or other resources). For brevity and clarity, thistransaction sequence primarily focuses on object 1703A, but embodimentsare not limited as such.

The transaction sequence continues with FIG. 17B with selection of a fewrandom points in objects 1703A and 1703B to serve as visual hull points1707A and 1707B within visual hulls 1705A and 1705B, respectively, asdetected and selected by detection and selection logic 1601 and beprocessed for visual hulling as facilitated by coarse logic 1603 of FIG.16. For example, visual hull points 1707A, 1707B are evaluated in eachgrid cell of search grid 1700 to determine whether they are contained invisual hulls 1705A, 1705B of cameras 1701, where visual hull points1707A, 1707B inside visual hulls 1705A, 1705B are retained to form thecorresponding point cloud clusters as facilitated by coarse logic 1603of FIG. 16.

The transaction sequence continues with FIG. 17C, where visual hullpoints 1707A, 1707B of FIG. 17B are now converted into correspondingsets of refined points 1709A, 1709B within search grid 1700 asfacilitated by coarse logic 1603 of FIG. 16. In one embodiment, anycells associated with or surrounding objects 1703A, 1703B and containingrefined points 1709A, 1709B are divided into subdivision cells 1711A,1711B. In one embodiment, subdivided cells 1711A, 1711B contain refinedpoints 1709A, 1709B, while point cloud generation is repeated with morepoints at finer grid resolution in subdivided cells 1711A, 1711B, asfacilitated by coarse logic 1603 of FIG. 16. This process of subdivisionand sampling is repeated until clusters capture the gross shape andlocation of objects 1703A, 1703B, as facilitated by coarse logic 1603 ofFIG. 16.

FIG. 17D illustrates construction of dense voxel grids 1713A, 1713Baround objects 1703A, 1703B, respectively, within search grid 1700. Inone embodiment, dense voxel grids 1713A, 1713B are positioned aroundobjects 1703A, 1703B, respectively, using clusters to inform the bestshape and size for each of dense voxel grids 1713A, 1713B, asfacilitated by fine logic 1605 of FIG. 16.

The transaction sequence continues with FIG. 17E, where, in oneembodiment, for each voxel in dense voxel grid 1713A, a determination ismade as to whether the voxel is inside or outside visual hull 1705A asfacilitated by fine logic 1605 of FIG. 16. For example, as illustrated,any voxels determined to be inside visual hull 1705A are regarded andlabeled as voxels inside visual hull 1715A, while any voxels determinedto be outside visual hull 1705A are regarded and labeled as voxelsoutside visual hull 1715A as facilitated by fine logic 1605 of FIG. 16.

The transaction sequence continues with FIG. 17F, where, in oneembodiment, level set construction of visual hull 1705A is performed asfacilitated by computation logic 1609. For example, using computationlogic 1609, various level sets are assigned to visual hull 1705A bycomputing signed distance field (e.g., level set) from the boundary ofvisual hull 1705A, where level set equals 0 1714A (at the inner boundaryof visual hull 1705), level set greater than 0 1714B (between the outerand inner boundaries of visual hull 1705), level set is less than 01714C (within the inner core of visual hull 1705), and level set isunknown 1714D (outside visual hull 1705), etc., as facilitated bycomputation logic 1609 of FIG. 16.

FIG. 17G represents the next transaction of the transaction sequence,where, in one embodiment, narrow-band cloud points are generated nearthe visual boundary of visual hull 1705A using the level set and itsgradients to compute the appropriate points and the normal such thatvisual hull 1705A is transformed into narrow-band cloud points-basedvisual hull, as facilitated by computation logic 1609 and/or visibilitylogic 1611 of FIG. 16. As illustrated one set of cloud points are placedbetween the outer and inner boundaries of visual hull 1705A, whileanother set of cloud points are placed inside the inner boundary ofvisual hull 1705A.

The transaction sequence continues with aggregation of points onboundary as illustrated in FIG. 17H, where, in one embodiment, linesearches are performed on each point along normal direction to positionthem on zero level set (boundary) of a visual hull, such as theillustrated transformed visual hull 1705A as facilitated by visibilitylogic 1611 of FIG. 16. As illustrated, the two sets of cloud points arenot merged into a single set of cloud points placed on the innerboundary of visual hull 1705A.

The transaction sequence continues at FIG. 17I with photometricoptimization as facilitated by photo-consistency logic 1613 of FIG. 16.As described previously with respect to FIG. 16, photo-consistency logic1613 of FIG. 16 may be used to perform line searches (e.g.,identification searches) on each cloud point long a normal direction tooptimize photo-consistency between multiple camera views, resulting inanother transformed visual hull 1705A. For example, this photometricoptimization allowed for consistency in colors such that the objects andtheir scenes are scalable.

For example, this novel technique of photo-consistency and smoothnessallows for occlusion tests for achieving color matching, resulting avariety of visual advantages to end-users, such as better computed andmapped colors, clearer boundaries, geometrical accuracy and coverage ofobjects, boundaries, proper distribution of points, scalability, and/orthe like. This novel technique also allows for saving of systemresources, such as computational power, memory, etc.

The transaction sequence continues with performance of cloud pointregulation at FIG. 17J, where cloud points are adjusted using thenearest-neighbor information to create even distribution of points,smoothing of normals, pruning of inconsistent or isolated points, etc.,as facilitated by photo-consistency logic 1613 of FIG. 16. In oneembodiment, cloud point regulation allows for cleaning up of cloudpoints such that certain clouds points are pruned or smoothed out andevenly distributed along the boundary of object 1703A within thetransformed visual hull 1705A.

FIG. 17K illustrates out-of-core reconstruction in the transactionsequence, where, for example, upon completing the transactions describedabove, completed block 1747 (representing dense voxel grid 1713A) isproduced as a result having a final-version of object 1703A asfacilitated by refinement and application logic 1615 of FIG. 16. Adescribed above, objects 1703A, 1703B may be processed together orseparately depending on the availability of memory, system resources,etc. In the illustrated embodiment, the processing of object 1703A iscompleted, such as competed block 1747, representing dense voxel grid1713A, while the processing of object 1703B is started as part of nextcompute block 1749 representing dense voxel grid 1713B.

FIG. 18 illustrate a method 1800 for facilitating point cloudreconstruction of scenes according to one embodiment. For brevity, manyof the details previously discussed with reference to FIGS. 1-17K maynot be discussed or repeated hereafter. Any processes relating to method1800 may be performed by processing logic that may comprise hardware(e.g., circuitry, dedicated logic, programmable logic, etc.), software(such as instructions run on a processing device), or a combinationthereof, as facilitated by reconstruction mechanism 1510 of FIG. 15. Theprocesses associated with method 1800 may be illustrated or recited inlinear sequences for brevity and clarity in presentation; however, it iscontemplated that any number of them can be performed in parallel,asynchronously, or in different orders.

Method 1800 begins at block 1801 with detection and selection of a sceneand its objects for purposes of reconstruction. At block 1803, initialsearch grid is generated across the scene for identifying visual hullsaround the objects, where visual hulls refer to the overlapping areasaround the objects are projected by cameras. At block 1805, visual hullcloud points are generated within each object to form point cloudclusters. At block 1807, cell subdivisions are introduced across cellsoccupying or having at least a portion of an object, while visual hullpoint cloud refinement is performed for cloud points within the objects.At block 1809, a combination of cell subdivisions occupying at least aportion of each object is selected for dense grid construction such thatdense voxel grids are positioned around objects using cluster to bestshape and size each dense grid for its corresponding object.

At block 1811, a portion of each dense voxel grids is carved such thatthe boundary of this carved shaped touches or remains within a proximatedistance of its object, where the voxels inside the carved region arereferred to as voxels inside visual hull, while those voxels that areoutside the carved region are referred to as voxels outside visual hullto allow for determination of voxels both inside and outside visualhull. At block 1813, signed distance fields (level sets) are computedfrom visual hull boundary to identify outside and inside boundaries ofthe carved region.

At block 1815, narrow-band points are generated near the visual boundaryusing level sets and their gradient to compute appropriate points andnormals such that points are cloud placed both inside the insideboundary and between the inside and outside boundaries of the carvedregion within the visual hull. At block 1817, the cloud points areaggregated on the inside boundary by performing line searches on eachpoint along normal direction to position them on zero level set(boundary) of the visual hull.

In one embodiment, at block 1819, photometric optimization of the carvedregion and the visual hull is performed by conducting line searches oneach point long a normal direction to optimize photo-consistency betweencamera views. At block 1821, point cloud regularization is performedsuch that point cloud is adjusted using the nearest-neighbor informationto provide for an even distribution of points and smooth normals bypruning inconsistent and/or isolated points. At block 1823, out-of-corereconstruction is performed such that dense point cloud reconstructionis repeated for each block and then all point clouds are merged togetherand, in some embodiments, method 1800 is repeated for any other objectsneeding reconstruction. At block 1825, results are accumulated andpresented as a reconstructed scene using display devices for viewing ofusers.

References to “one embodiment”, “an embodiment”, “example embodiment”,“various embodiments”, etc., indicate that the embodiment(s) sodescribed may include particular features, structures, orcharacteristics, but not every embodiment necessarily includes theparticular features, structures, or characteristics. Further, someembodiments may have some, all, or none of the features described forother embodiments.

In the foregoing specification, embodiments have been described withreference to specific exemplary embodiments thereof. It will, however,be evident that various modifications and changes may be made theretowithout departing from the broader spirit and scope of embodiments asset forth in the appended claims. The Specification and drawings are,accordingly, to be regarded in an illustrative rather than a restrictivesense.

In the following description and claims, the term “coupled” along withits derivatives, may be used. “Coupled” is used to indicate that two ormore elements co-operate or interact with each other, but they may ormay not have intervening physical or electrical components between them.

As used in the claims, unless otherwise specified the use of the ordinaladjectives “first”, “second”, “third”, etc., to describe a commonelement, merely indicate that different instances of like elements arebeing referred to, and are not intended to imply that the elements sodescribed must be in a given sequence, either temporally, spatially, inranking, or in any other manner.

The following clauses and/or examples pertain to further embodiments orexamples. Specifics in the examples may be used anywhere in one or moreembodiments. The various features of the different embodiments orexamples may be variously combined with some features included andothers excluded to suit a variety of different applications. Examplesmay include subject matter such as a method, means for performing actsof the method, at least one machine-readable medium includinginstructions that, when performed by a machine cause the machine toperform acts of the method, or of an apparatus or system forfacilitating hybrid communication according to embodiments and examplesdescribed herein.

Some embodiments pertain to Example 1 that includes an apparatus tofacilitate smart point cloud reconstruction of objects in visual scenesin computing environments, the apparatus comprising: one or moreprocessors including one or more graphics processors; photo-consistencylogic to perform line searches on cloud points of an object to enhancephoto-consistency between multiple camera views associated with a visualhull encompassing the object in a scene captured by multiple camerascoupled to the one or more processors; and refinement and applicationlogic to perform a final reconstruction of the object based on theenhanced photo-consistency, wherein the scene having the reconstructedobject is displayed at a display device.

Example 2 includes the subject matter of Example 1, further comprisingdetection and selection logic to detect and select the scene havingobjects for reconstruction, wherein the objects include the object.

Example 3 includes the subject matter of Examples 1-2, furthercomprising coarse visual hulling logic (“coarse logic”) to generate aninitial search grid for visual hull clustering of the objects such thatthe objects to be reconstructed are shown in visual hulls representingoverlapping of the multiple camera views associated with the multiplecameras, wherein the coarse logic is further to perform visual hullpoint cloud refinement of cloud points of the objects.

Example 4 includes the subject matter of Examples 1-3, furthercomprising fine visual hulling logic (“fine logic”) to construct a densevoxel grid in segmented cells having at least a portion of the object,wherein the fine logic is further to carve out voxels from the densevoxel grid, wherein the voxels encompass the object and are presentinside and outside of the visual hull.

Example 5 includes the subject matter of Examples 1-4, furthercomprising computation logic to compute signed distance fieldsrepresenting level sets from an inner boundary and an outer boundary ofthe visual hull.

Example 6 includes the subject matter of Examples 1-5, furthercomprising visibility logic to generate a point cloud having narrow-bandpoints near the inner and outer boundaries of the visual hull, whereinthe visibility logic is further to aggregate the narrow-band points onthe inner boundary of the visual hull.

Example 7 includes the subject matter of Examples 1-6, wherein the oneor more graphics processors are co-located with one or more applicationprocessors on a common semiconductor package.

Some embodiments pertain to Example 8 that includes a method forfacilitating smart convolution in machine learning environments, themethod comprising: performing line searches on cloud points of an objectto enhance photo-consistency between multiple camera views associatedwith a visual hull encompassing the object in a scene captured bymultiple cameras coupled to the one or more processors including one ormore graphics processors; and performing a final reconstruction of theobject based on the enhanced photo-consistency, wherein the scene havingthe reconstructed object is displayed at a display device.

Example 9 includes the subject matter of Example 8, further comprisingdetecting and selecting the scene having objects for reconstruction,wherein the objects include the object.

Example 10 includes the subject matter of Examples 8-9, furthercomprising: generating an initial search grid for visual hull clusteringof the objects such that the objects to be reconstructed are shown invisual hulls representing overlapping of the multiple camera viewsassociated with the multiple cameras; and performing visual hull pointcloud refinement of cloud points of the objects.

Example 11 includes the subject matter of Examples 8-10, furthercomprising constructing a dense voxel grid in segmented cells having atleast a portion of the object; and carving out voxels from the densevoxel grid, wherein the voxels encompass the object and are presentinside and outside of the visual hull.

Example 12 includes the subject matter of Examples 8-11, furthercomprising computing signed distance fields representing level sets froman inner boundary and an outer boundary of the visual hull.

Example 13 includes the subject matter of Examples 8-12, furthercomprising: generating a point cloud having narrow-band points near theinner and outer boundaries of the visual hull; and aggregating thenarrow-band points on the inner boundary of the visual hull.

Example 14 includes the subject matter of Examples 8-13, wherein the oneor more graphics processors are co-located with one or more applicationprocessors on a common semiconductor package.

Some embodiments pertain to Example 15 includes a data processing systemcomprising a storage device having instructions, and a processing deviceto execute the instructions, the processing device to: perform linesearches on cloud points of an object to enhance photo-consistencybetween multiple camera views associated with a visual hull encompassingthe object in a scene captured by multiple cameras coupled to the one ormore processors including one or more graphics processors; and perform afinal reconstruction of the object based on the enhancedphoto-consistency, wherein the scene having the reconstructed object isdisplayed at a display device.

Example 16 includes the subject matter of Example 15, wherein theprocessing device is further to: detect and select the scene havingobjects for reconstruction, wherein the objects include the object.

Example 17 includes the subject matter of Examples 15-16, wherein theprocessing device is further to: generate an initial search grid forvisual hull clustering of the objects such that the objects to bereconstructed are shown in visual hulls representing overlapping of themultiple camera views associated with the multiple cameras; and performvisual hull point cloud refinement of cloud points of the objects.

Example 18 includes the subject matter of Examples 15-17, wherein theprocessing device is further to construct a dense voxel grid insegmented cells having at least a portion of the object; and carving outvoxels from the dense voxel grid, wherein the voxels encompass theobject and are present inside and outside of the visual hull.

Example 19 includes the subject matter of Examples 15-18, wherein theprocessing device is further to compute signed distance fieldsrepresenting level sets from an inner boundary and an outer boundary ofthe visual hull.

Example 20 includes the subject matter of Examples 15-19, wherein theprocessing device is further to: generate a point cloud havingnarrow-band points near the inner and outer boundaries of the visualhull; and aggregate the narrow-band points on the inner boundary of thevisual hull.

Example 21 includes the subject matter of Examples 15-20, wherein theone or more graphics processors are co-located with one or moreapplication processors on a common semiconductor package.

Some embodiments pertain to Example 22 includes an apparatus comprising:means for performing line searches on cloud points of an object toenhance photo-consistency between multiple camera views associated witha visual hull encompassing the object in a scene captured by multiplecameras coupled to the one or more processors including one or moregraphics processors; and means for performing a final reconstruction ofthe object based on the enhanced photo-consistency, wherein the scenehaving the reconstructed object is displayed at a display device.

Example 23 includes the subject matter of Example 22, further comprisingmeans for detecting and selecting the scene having objects forreconstruction, wherein the objects include the object.

Example 24 includes the subject matter of Examples 22-23, furthercomprising generating an initial search grid for visual hull clusteringof the objects such that the objects to be reconstructed are shown invisual hulls representing overlapping of the multiple camera viewsassociated with the multiple cameras; and perform visual hull pointcloud refinement of cloud points of the objects.

Example 25 includes the subject matter of Examples 22-24, furthercomprising means for constructing a dense voxel grid in segmented cellshaving at least a portion of the object; and carving out voxels from thedense voxel grid, wherein the voxels encompass the object and arepresent inside and outside of the visual hull.

Example 26 includes the subject matter of Examples 22-25, furthercomprising means for computing signed distance fields representing levelsets from an inner boundary and an outer boundary of the visual hull.

Example 27 includes the subject matter of Examples 22-26, furthercomprising means for generating a point cloud having narrow-band pointsnear the inner and outer boundaries of the visual hull; and aggregatethe narrow-band points on the inner boundary of the visual hull.

Example 28 includes the subject matter of Examples 22-27, wherein theone or more graphics processors are co-located with one or moreapplication processors on a common semiconductor package.

Example 29 includes at least one non-transitory or tangiblemachine-readable medium comprising a plurality of instructions, whenexecuted on a computing device, to implement or perform a method asclaimed in any of claims or examples 8-14.

Example 30 includes at least one machine-readable medium comprising aplurality of instructions, when executed on a computing device, toimplement or perform a method as claimed in any of claims or examples8-14.

Example 31 includes a system comprising a mechanism to implement orperform a method as claimed in any of claims or examples 8-14.

Example 32 includes an apparatus comprising means for performing amethod as claimed in any of claims or examples 8-14.

Example 33 includes a computing device arranged to implement or performa method as claimed in any of claims or examples 8-14.

Example 34 includes a communications device arranged to implement orperform a method as claimed in any of claims or examples 8-14.

Example 35 includes at least one machine-readable medium comprising aplurality of instructions, when executed on a computing device, toimplement or perform a method or realize an apparatus as claimed in anypreceding claims.

Example 36 includes at least one non-transitory or tangiblemachine-readable medium comprising a plurality of instructions, whenexecuted on a computing device, to implement or perform a method orrealize an apparatus as claimed in any preceding claims.

Example 37 includes a system comprising a mechanism to implement orperform a method or realize an apparatus as claimed in any precedingclaims.

Example 38 includes an apparatus comprising means to perform a method asclaimed in any preceding claims.

Example 39 includes a computing device arranged to implement or performa method or realize an apparatus as claimed in any preceding claims.

Example 40 includes a communications device arranged to implement orperform a method or realize an apparatus as claimed in any precedingclaims.

The drawings and the forgoing description give examples of embodiments.Those skilled in the art will appreciate that one or more of thedescribed elements may well be combined into a single functionalelement. Alternatively, certain elements may be split into multiplefunctional elements. Elements from one embodiment may be added toanother embodiment. For example, orders of processes described hereinmay be changed and are not limited to the manner described herein.Moreover, the actions of any flow diagram need not be implemented in theorder shown; nor do all of the acts necessarily need to be performed.Also, those acts that are not dependent on other acts may be performedin parallel with the other acts. The scope of embodiments is by no meanslimited by these specific examples. Numerous variations, whetherexplicitly given in the specification or not, such as differences instructure, dimension, and use of material, are possible. The scope ofembodiments is at least as broad as given by the following claims.

What is claimed is:
 1. An apparatus comprising: one or more processorsincluding one or more graphics processors; photo-consistency logic toperform line searches on cloud points of an object to enhancephoto-consistency between multiple camera views associated with a visualhull encompassing the object in a scene captured by multiple camerascoupled to the one or more processors; and refinement and applicationlogic to perform a final reconstruction of the object based on theenhanced photo-consistency, wherein the scene having the reconstructedobject is displayed at a display device.
 2. The apparatus of claim 1,further comprising detection and selection logic to detect and selectthe scene having objects for reconstruction, wherein the objects includethe object.
 3. The apparatus of claim 1, further comprising coarsevisual hulling logic (“coarse logic”) to generate an initial search gridfor visual hull clustering of the objects such that the objects to bereconstructed are shown in visual hulls representing overlapping of themultiple camera views associated with the multiple cameras, wherein thecoarse logic is further to perform visual hull point cloud refinement ofcloud points of the objects.
 4. The apparatus of claim 1, furthercomprising fine visual hulling logic (“fine logic”) to construct a densevoxel grid in segmented cells having at least a portion of the object,wherein the fine logic is further to carve out voxels from the densevoxel grid, wherein the voxels encompass the object and are presentinside and outside of the visual hull.
 5. The apparatus of claim 1,further comprising computation logic to compute signed distance fieldsrepresenting level sets from an inner boundary and an outer boundary ofthe visual hull.
 6. The apparatus of claim 1, further comprisingvisibility logic to generate a point cloud having narrow-band pointsnear the inner and outer boundaries of the visual hull, wherein thevisibility logic is further to aggregate the narrow-band points on theinner boundary of the visual hull.
 7. The apparatus of claim 1, whereinthe one or more graphics processors are co-located with one or moreapplication processors on a common semiconductor package.
 8. A methodcomprising: performing line searches on cloud points of an object toenhance photo-consistency between multiple camera views associated witha visual hull encompassing the object in a scene captured by multiplecameras coupled to the one or more processors including one or moregraphics processors; and performing a final reconstruction of the objectbased on the enhanced photo-consistency, wherein the scene having thereconstructed object is displayed at a display device.
 9. The method ofclaim 8, further comprising detecting and selecting the scene havingobjects for reconstruction, wherein the objects include the object. 10.The method of claim 8, further comprising: generating an initial searchgrid for visual hull clustering of the objects such that the objects tobe reconstructed are shown in visual hulls representing overlapping ofthe multiple camera views associated with the multiple cameras; andperforming visual hull point cloud refinement of cloud points of theobjects.
 11. The method of claim 8, further comprising: constructing adense voxel grid in segmented cells having at least a portion of theobject; and carving out voxels from the dense voxel grid, wherein thevoxels encompass the object and are present inside and outside of thevisual hull.
 12. The method of claim 8, further comprising computingsigned distance fields representing level sets from an inner boundaryand an outer boundary of the visual hull.
 13. The method of claim 8,further comprising: generating a point cloud having narrow-band pointsnear the inner and outer boundaries of the visual hull; and aggregatingthe narrow-band points on the inner boundary of the visual hull.
 14. Themethod of claim 8, wherein the one or more graphics processors areco-located with one or more application processors on a commonsemiconductor package.
 15. At least one machine-readable mediumcomprising a plurality of instructions which, when executed on acomputing device, cause the computing device to perform operationscomprising: performing line searches on cloud points of an object toenhance photo-consistency between multiple camera views associated witha visual hull encompassing the object in a scene captured by multiplecameras coupled to the one or more processors including one or moregraphics processors; and performing a final reconstruction of the objectbased on the enhanced photo-consistency, wherein the scene having thereconstructed object is displayed at a display device.
 16. Themachine-readable medium of claim 15, wherein the operations furthercomprise detecting and selecting the scene having objects forreconstruction, wherein the objects include the object.
 17. Themachine-readable medium of claim 15, wherein the operations furthercomprise: generating an initial search grid for visual hull clusteringof the objects such that the objects to be reconstructed are shown invisual hulls representing overlapping of the multiple camera viewsassociated with the multiple cameras; and performing visual hull pointcloud refinement of cloud points of the objects.
 18. Themachine-readable medium of claim 15, wherein the operations furthercomprise: constructing a dense voxel grid in segmented cells having atleast a portion of the object; and carving out voxels from the densevoxel grid, wherein the voxels encompass the object and are presentinside and outside of the visual hull.
 19. The machine-readable mediumof claim 15, wherein the operations further comprise computing signeddistance fields representing level sets from an inner boundary and anouter boundary of the visual hull.
 20. The machine-readable medium ofclaim 15, wherein the operations further comprise: generating a pointcloud having narrow-band points near the inner and outer boundaries ofthe visual hull; and aggregating the narrow-band points on the innerboundary of the visual hull, wherein the one or more graphics processorsare co-located with one or more application processors on a commonsemiconductor package.